id	sid	tid	token	lemma	pos
ijassa-899	1	1	adv	adv	PROPN
ijassa-899	1	2	syst	syst	PROPN
ijassa-899	1	3	sci	sci	PROPN
ijassa-899	1	4	appl	appl	PROPN
ijassa-899	1	5	2020	2020	NUM
ijassa-899	1	6	;	;	PUNCT
ijassa-899	1	7	02:98–118	02:98–118	NUM
ijassa-899	1	8	published	publish	VERB
ijassa-899	1	9	online	online	ADV
ijassa-899	1	10	at	at	ADP
ijassa-899	1	11	https://ijassa.ipu.ru	https://ijassa.ipu.ru	ADJ
ijassa-899	1	12	.	.	PUNCT
ijassa-899	2	1	student	student	NOUN
ijassa-899	2	2	mixture	mixture	NOUN
ijassa-899	2	3	and	and	CCONJ
ijassa-899	2	4	its	its	PRON
ijassa-899	2	5	machine	machine	NOUN
ijassa-899	2	6	learning	learn	VERB
ijassa-899	2	7	applications	application	NOUN
ijassa-899	2	8	to	to	ADP
ijassa-899	2	9	pvt	pvt	PROPN
ijassa-899	2	10	properties	property	NOUN
ijassa-899	2	11	of	of	ADP
ijassa-899	2	12	reservoir	reservoir	NOUN
ijassa-899	2	13	fluids	fluid	NOUN
ijassa-899	2	14	nikita	nikita	PROPN
ijassa-899	2	15	volkov1,2	volkov1,2	PROPN
ijassa-899	2	16	*	*	PROPN
ijassa-899	2	17	,	,	PUNCT
ijassa-899	2	18	elizaveta	elizaveta	PROPN
ijassa-899	2	19	dakhova1,2	dakhova1,2	PROPN
ijassa-899	2	20	,	,	PUNCT
ijassa-899	2	21	semen	semen	NOUN
ijassa-899	2	22	budennyy1,2	budennyy1,2	PROPN
ijassa-899	2	23	,	,	PUNCT
ijassa-899	2	24	alla	alla	NOUN
ijassa-899	2	25	andrianova3	andrianova3	PROPN
ijassa-899	3	1	1moscow	1moscow	NUM
ijassa-899	3	2	institute	institute	PROPN
ijassa-899	3	3	of	of	ADP
ijassa-899	3	4	physics	physics	PROPN
ijassa-899	3	5	and	and	CCONJ
ijassa-899	3	6	technology	technology	NOUN
ijassa-899	3	7	,	,	PUNCT
ijassa-899	3	8	moscow	moscow	PROPN
ijassa-899	3	9	,	,	PUNCT
ijassa-899	3	10	russia	russia	PROPN
ijassa-899	3	11	2center	2center	NUM
ijassa-899	3	12	for	for	ADP
ijassa-899	3	13	engineering	engineering	NOUN
ijassa-899	3	14	and	and	CCONJ
ijassa-899	3	15	technology	technology	NOUN
ijassa-899	3	16	of	of	ADP
ijassa-899	3	17	mipt	mipt	ADJ
ijassa-899	3	18	,	,	PUNCT
ijassa-899	3	19	moscow	moscow	PROPN
ijassa-899	3	20	,	,	PUNCT
ijassa-899	3	21	russia	russia	PROPN
ijassa-899	3	22	3gazprom	3gazprom	NUM
ijassa-899	3	23	neft	neft	VERB
ijassa-899	3	24	science	science	NOUN
ijassa-899	3	25	and	and	CCONJ
ijassa-899	3	26	technology	technology	NOUN
ijassa-899	3	27	center	center	NOUN
ijassa-899	3	28	,	,	PUNCT
ijassa-899	3	29	st.-petersburg	st.-petersburg	NOUN
ijassa-899	3	30	,	,	PUNCT
ijassa-899	3	31	russia	russia	PROPN
ijassa-899	3	32	abstract	abstract	NOUN
ijassa-899	3	33	:	:	PUNCT
ijassa-899	3	34	distribution	distribution	NOUN
ijassa-899	3	35	mixture	mixture	NOUN
ijassa-899	3	36	models	model	NOUN
ijassa-899	3	37	are	be	AUX
ijassa-899	3	38	widely	widely	ADV
ijassa-899	3	39	used	use	VERB
ijassa-899	3	40	in	in	ADP
ijassa-899	3	41	cluster	cluster	NOUN
ijassa-899	3	42	analysis	analysis	NOUN
ijassa-899	3	43	.	.	PUNCT
ijassa-899	4	1	particularly	particularly	ADV
ijassa-899	4	2	,	,	PUNCT
ijassa-899	4	3	a	a	DET
ijassa-899	4	4	mixture	mixture	NOUN
ijassa-899	4	5	of	of	ADP
ijassa-899	4	6	student	student	NOUN
ijassa-899	4	7	t	t	PROPN
ijassa-899	4	8	-	-	PUNCT
ijassa-899	4	9	distributions	distribution	NOUN
ijassa-899	4	10	is	be	AUX
ijassa-899	4	11	mostly	mostly	ADV
ijassa-899	4	12	applied	apply	VERB
ijassa-899	4	13	for	for	ADP
ijassa-899	4	14	robust	robust	ADJ
ijassa-899	4	15	data	datum	NOUN
ijassa-899	4	16	clustering	cluster	VERB
ijassa-899	4	17	.	.	PUNCT
ijassa-899	5	1	in	in	ADP
ijassa-899	5	2	this	this	DET
ijassa-899	5	3	paper	paper	NOUN
ijassa-899	5	4	,	,	PUNCT
ijassa-899	5	5	we	we	PRON
ijassa-899	5	6	introduce	introduce	VERB
ijassa-899	5	7	em	em	PRON
ijassa-899	5	8	algorithm	algorithm	NOUN
ijassa-899	5	9	for	for	ADP
ijassa-899	5	10	a	a	DET
ijassa-899	5	11	mixture	mixture	NOUN
ijassa-899	5	12	of	of	ADP
ijassa-899	5	13	student	student	NOUN
ijassa-899	5	14	distributions	distribution	NOUN
ijassa-899	5	15	,	,	PUNCT
ijassa-899	5	16	where	where	SCONJ
ijassa-899	5	17	at	at	ADP
ijassa-899	5	18	the	the	DET
ijassa-899	5	19	e	e	NOUN
ijassa-899	5	20	-	-	NOUN
ijassa-899	5	21	step	step	NOUN
ijassa-899	5	22	,	,	PUNCT
ijassa-899	5	23	we	we	PRON
ijassa-899	5	24	apply	apply	VERB
ijassa-899	5	25	variational	variational	ADJ
ijassa-899	5	26	bayesian	bayesian	NOUN
ijassa-899	5	27	inference	inference	NOUN
ijassa-899	5	28	for	for	ADP
ijassa-899	5	29	parameters	parameter	NOUN
ijassa-899	5	30	estimation	estimation	NOUN
ijassa-899	5	31	.	.	PUNCT
ijassa-899	6	1	based	base	VERB
ijassa-899	6	2	on	on	ADP
ijassa-899	6	3	the	the	DET
ijassa-899	6	4	mixture	mixture	NOUN
ijassa-899	6	5	of	of	ADP
ijassa-899	6	6	student	student	NOUN
ijassa-899	6	7	distributions	distribution	NOUN
ijassa-899	6	8	,	,	PUNCT
ijassa-899	6	9	we	we	PRON
ijassa-899	6	10	construct	construct	VERB
ijassa-899	6	11	a	a	DET
ijassa-899	6	12	machine	machine	NOUN
ijassa-899	6	13	learning	learning	NOUN
ijassa-899	6	14	method	method	NOUN
ijassa-899	6	15	that	that	PRON
ijassa-899	6	16	allows	allow	VERB
ijassa-899	6	17	to	to	PART
ijassa-899	6	18	solve	solve	VERB
ijassa-899	6	19	regression	regression	NOUN
ijassa-899	6	20	problems	problem	NOUN
ijassa-899	6	21	for	for	ADP
ijassa-899	6	22	any	any	DET
ijassa-899	6	23	set	set	NOUN
ijassa-899	6	24	of	of	ADP
ijassa-899	6	25	features	feature	NOUN
ijassa-899	6	26	,	,	PUNCT
ijassa-899	6	27	clustering	clustering	NOUN
ijassa-899	6	28	,	,	PUNCT
ijassa-899	6	29	and	and	CCONJ
ijassa-899	6	30	anomaly	anomaly	NOUN
ijassa-899	6	31	detection	detection	NOUN
ijassa-899	6	32	within	within	ADP
ijassa-899	6	33	one	one	NUM
ijassa-899	6	34	model	model	NOUN
ijassa-899	6	35	.	.	PUNCT
ijassa-899	7	1	each	each	PRON
ijassa-899	7	2	of	of	ADP
ijassa-899	7	3	these	these	DET
ijassa-899	7	4	problems	problem	NOUN
ijassa-899	7	5	can	can	AUX
ijassa-899	7	6	be	be	AUX
ijassa-899	7	7	solved	solve	VERB
ijassa-899	7	8	by	by	ADP
ijassa-899	7	9	the	the	DET
ijassa-899	7	10	model	model	NOUN
ijassa-899	7	11	even	even	ADV
ijassa-899	7	12	if	if	SCONJ
ijassa-899	7	13	there	there	PRON
ijassa-899	7	14	are	be	VERB
ijassa-899	7	15	missing	miss	VERB
ijassa-899	7	16	values	value	NOUN
ijassa-899	7	17	in	in	ADP
ijassa-899	7	18	the	the	DET
ijassa-899	7	19	data	datum	NOUN
ijassa-899	7	20	.	.	PUNCT
ijassa-899	8	1	the	the	DET
ijassa-899	8	2	proposed	propose	VERB
ijassa-899	8	3	method	method	NOUN
ijassa-899	8	4	was	be	AUX
ijassa-899	8	5	tested	test	VERB
ijassa-899	8	6	on	on	ADP
ijassa-899	8	7	real	real	ADJ
ijassa-899	8	8	data	datum	NOUN
ijassa-899	8	9	describing	describe	VERB
ijassa-899	8	10	the	the	DET
ijassa-899	8	11	pvt	pvt	PROPN
ijassa-899	8	12	properties	property	NOUN
ijassa-899	8	13	of	of	ADP
ijassa-899	8	14	reservoir	reservoir	NOUN
ijassa-899	8	15	fluids	fluid	NOUN
ijassa-899	8	16	.	.	PUNCT
ijassa-899	9	1	the	the	DET
ijassa-899	9	2	results	result	NOUN
ijassa-899	9	3	obtained	obtain	VERB
ijassa-899	9	4	by	by	ADP
ijassa-899	9	5	the	the	DET
ijassa-899	9	6	model	model	NOUN
ijassa-899	9	7	do	do	AUX
ijassa-899	9	8	not	not	PART
ijassa-899	9	9	contradict	contradict	VERB
ijassa-899	9	10	the	the	DET
ijassa-899	9	11	basic	basic	ADJ
ijassa-899	9	12	physical	physical	ADJ
ijassa-899	9	13	properties	property	NOUN
ijassa-899	9	14	.	.	PUNCT
ijassa-899	10	1	in	in	ADP
ijassa-899	10	2	majority	majority	NOUN
ijassa-899	10	3	of	of	ADP
ijassa-899	10	4	conducted	conduct	VERB
ijassa-899	10	5	experiments	experiment	NOUN
ijassa-899	10	6	our	our	PRON
ijassa-899	10	7	model	model	NOUN
ijassa-899	10	8	gives	give	VERB
ijassa-899	10	9	more	more	ADV
ijassa-899	10	10	accurate	accurate	ADJ
ijassa-899	10	11	results	result	NOUN
ijassa-899	10	12	than	than	ADP
ijassa-899	10	13	well	well	ADV
ijassa-899	10	14	-	-	PUNCT
ijassa-899	10	15	known	know	VERB
ijassa-899	10	16	machine	machine	NOUN
ijassa-899	10	17	learning	learning	NOUN
ijassa-899	10	18	methods	method	NOUN
ijassa-899	10	19	in	in	ADP
ijassa-899	10	20	terms	term	NOUN
ijassa-899	10	21	of	of	ADP
ijassa-899	10	22	mape	mape	NOUN
ijassa-899	10	23	and	and	CCONJ
ijassa-899	10	24	rmspe	rmspe	ADJ
ijassa-899	10	25	metrics	metric	NOUN
ijassa-899	10	26	.	.	PUNCT
ijassa-899	11	1	keywords	keyword	NOUN
ijassa-899	11	2	:	:	PUNCT
ijassa-899	11	3	student	student	NOUN
ijassa-899	11	4	mixture	mixture	NOUN
ijassa-899	11	5	,	,	PUNCT
ijassa-899	11	6	em	em	PRON
ijassa-899	11	7	algorithm	algorithm	NOUN
ijassa-899	11	8	,	,	PUNCT
ijassa-899	11	9	variational	variational	ADJ
ijassa-899	11	10	bayesian	bayesian	NOUN
ijassa-899	11	11	inference	inference	NOUN
ijassa-899	11	12	,	,	PUNCT
ijassa-899	11	13	clustering	clustering	NOUN
ijassa-899	11	14	,	,	PUNCT
ijassa-899	11	15	regression	regression	NOUN
ijassa-899	11	16	,	,	PUNCT
ijassa-899	11	17	anomalies	anomaly	NOUN
ijassa-899	11	18	,	,	PUNCT
ijassa-899	11	19	missing	miss	VERB
ijassa-899	11	20	values	value	NOUN
ijassa-899	11	21	,	,	PUNCT
ijassa-899	11	22	pvt	pvt	PROPN
ijassa-899	11	23	properties	property	NOUN
ijassa-899	11	24	1	1	NUM
ijassa-899	11	25	.	.	PUNCT
ijassa-899	12	1	introduction	introduction	NOUN
ijassa-899	12	2	normal	normal	ADJ
ijassa-899	12	3	distributions	distribution	NOUN
ijassa-899	12	4	often	often	ADV
ijassa-899	12	5	occur	occur	VERB
ijassa-899	12	6	in	in	ADP
ijassa-899	12	7	various	various	ADJ
ijassa-899	12	8	data	datum	NOUN
ijassa-899	12	9	analysis	analysis	NOUN
ijassa-899	12	10	problems	problem	NOUN
ijassa-899	12	11	and	and	CCONJ
ijassa-899	12	12	they	they	PRON
ijassa-899	12	13	are	be	AUX
ijassa-899	12	14	fairly	fairly	ADV
ijassa-899	12	15	well	well	ADV
ijassa-899	12	16	studied	study	VERB
ijassa-899	12	17	.	.	PUNCT
ijassa-899	13	1	however	however	ADV
ijassa-899	13	2	,	,	PUNCT
ijassa-899	13	3	their	their	PRON
ijassa-899	13	4	disadvantage	disadvantage	NOUN
ijassa-899	13	5	for	for	ADP
ijassa-899	13	6	evaluating	evaluate	VERB
ijassa-899	13	7	parameters	parameter	NOUN
ijassa-899	13	8	is	be	AUX
ijassa-899	13	9	their	their	PRON
ijassa-899	13	10	light	light	ADJ
ijassa-899	13	11	distribution	distribution	NOUN
ijassa-899	13	12	tails	tail	NOUN
ijassa-899	13	13	.	.	PUNCT
ijassa-899	14	1	in	in	ADP
ijassa-899	14	2	the	the	DET
ijassa-899	14	3	presence	presence	NOUN
ijassa-899	14	4	of	of	ADP
ijassa-899	14	5	outliers	outlier	NOUN
ijassa-899	14	6	,	,	PUNCT
ijassa-899	14	7	as	as	SCONJ
ijassa-899	14	8	it	it	PRON
ijassa-899	14	9	usually	usually	ADV
ijassa-899	14	10	occurs	occur	VERB
ijassa-899	14	11	in	in	ADP
ijassa-899	14	12	real	real	ADJ
ijassa-899	14	13	problems	problem	NOUN
ijassa-899	14	14	,	,	PUNCT
ijassa-899	14	15	the	the	DET
ijassa-899	14	16	parameter	parameter	NOUN
ijassa-899	14	17	estimates	estimate	NOUN
ijassa-899	14	18	are	be	AUX
ijassa-899	14	19	strongly	strongly	ADV
ijassa-899	14	20	biased	biased	ADJ
ijassa-899	14	21	towards	towards	ADP
ijassa-899	14	22	outliers	outlier	NOUN
ijassa-899	14	23	.	.	PUNCT
ijassa-899	15	1	to	to	PART
ijassa-899	15	2	eliminate	eliminate	VERB
ijassa-899	15	3	this	this	DET
ijassa-899	15	4	disadvantage	disadvantage	NOUN
ijassa-899	15	5	,	,	PUNCT
ijassa-899	15	6	the	the	DET
ijassa-899	15	7	student	student	NOUN
ijassa-899	15	8	distribution	distribution	NOUN
ijassa-899	15	9	(	(	PUNCT
ijassa-899	15	10	or	or	CCONJ
ijassa-899	15	11	t	t	NOUN
ijassa-899	15	12	-	-	PUNCT
ijassa-899	15	13	distribution	distribution	NOUN
ijassa-899	15	14	)	)	PUNCT
ijassa-899	15	15	is	be	AUX
ijassa-899	15	16	often	often	ADV
ijassa-899	15	17	considered	consider	VERB
ijassa-899	15	18	,	,	PUNCT
ijassa-899	15	19	because	because	SCONJ
ijassa-899	15	20	its	its	PRON
ijassa-899	15	21	properties	property	NOUN
ijassa-899	15	22	are	be	AUX
ijassa-899	15	23	similar	similar	ADJ
ijassa-899	15	24	to	to	ADP
ijassa-899	15	25	those	those	PRON
ijassa-899	15	26	of	of	ADP
ijassa-899	15	27	the	the	DET
ijassa-899	15	28	normal	normal	ADJ
ijassa-899	15	29	distribution	distribution	NOUN
ijassa-899	15	30	,	,	PUNCT
ijassa-899	15	31	but	but	CCONJ
ijassa-899	15	32	it	it	PRON
ijassa-899	15	33	has	have	VERB
ijassa-899	15	34	heavy	heavy	ADJ
ijassa-899	15	35	tails	tail	NOUN
ijassa-899	15	36	.	.	PUNCT
ijassa-899	16	1	thus	thus	ADV
ijassa-899	16	2	,	,	PUNCT
ijassa-899	16	3	the	the	DET
ijassa-899	16	4	student	student	NOUN
ijassa-899	16	5	distribution	distribution	NOUN
ijassa-899	16	6	has	have	VERB
ijassa-899	16	7	a	a	DET
ijassa-899	16	8	certain	certain	ADJ
ijassa-899	16	9	degree	degree	NOUN
ijassa-899	16	10	of	of	ADP
ijassa-899	16	11	stability	stability	NOUN
ijassa-899	16	12	to	to	ADP
ijassa-899	16	13	emissions	emission	NOUN
ijassa-899	16	14	.	.	PUNCT
ijassa-899	17	1	the	the	DET
ijassa-899	17	2	properties	property	NOUN
ijassa-899	17	3	of	of	ADP
ijassa-899	17	4	the	the	DET
ijassa-899	17	5	student	student	NOUN
ijassa-899	17	6	distribution	distribution	NOUN
ijassa-899	17	7	were	be	AUX
ijassa-899	17	8	first	first	ADV
ijassa-899	17	9	studied	study	VERB
ijassa-899	17	10	by	by	ADP
ijassa-899	17	11	william	william	PROPN
ijassa-899	17	12	gossett	gossett	PROPN
ijassa-899	17	13	.	.	PUNCT
ijassa-899	18	1	the	the	DET
ijassa-899	18	2	author	author	NOUN
ijassa-899	18	3	has	have	AUX
ijassa-899	18	4	published	publish	VERB
ijassa-899	18	5	his	his	PRON
ijassa-899	18	6	first	first	ADJ
ijassa-899	18	7	results	result	NOUN
ijassa-899	18	8	on	on	ADP
ijassa-899	18	9	that	that	PRON
ijassa-899	18	10	under	under	ADP
ijassa-899	18	11	the	the	DET
ijassa-899	18	12	pseudonym	pseudonym	NOUN
ijassa-899	18	13	student	student	NOUN
ijassa-899	18	14	.	.	PUNCT
ijassa-899	19	1	gosset	gosset	PROPN
ijassa-899	19	2	noted	note	VERB
ijassa-899	19	3	that	that	SCONJ
ijassa-899	19	4	the	the	DET
ijassa-899	19	5	distribution	distribution	NOUN
ijassa-899	19	6	of	of	ADP
ijassa-899	19	7	the	the	DET
ijassa-899	19	8	standardized	standardized	ADJ
ijassa-899	19	9	(	(	PUNCT
ijassa-899	19	10	centered	center	VERB
ijassa-899	19	11	and	and	CCONJ
ijassa-899	19	12	scaled	scale	VERB
ijassa-899	19	13	)	)	PUNCT
ijassa-899	19	14	normal	normal	ADJ
ijassa-899	19	15	sample	sample	NOUN
ijassa-899	19	16	average	average	NOUN
ijassa-899	19	17	where	where	SCONJ
ijassa-899	19	18	the	the	DET
ijassa-899	19	19	unknown	unknown	ADJ
ijassa-899	19	20	variance	variance	NOUN
ijassa-899	19	21	is	be	AUX
ijassa-899	19	22	replaced	replace	VERB
ijassa-899	19	23	with	with	ADP
ijassa-899	19	24	its	its	PRON
ijassa-899	19	25	estimation	estimation	NOUN
ijassa-899	19	26	is	be	AUX
ijassa-899	19	27	different	different	ADJ
ijassa-899	19	28	from	from	ADP
ijassa-899	19	29	the	the	DET
ijassa-899	19	30	normal	normal	ADJ
ijassa-899	19	31	one	one	NUM
ijassa-899	20	1	[	[	X
ijassa-899	20	2	1	1	NUM
ijassa-899	20	3	]	]	PUNCT
ijassa-899	20	4	.	.	PUNCT
ijassa-899	21	1	there	there	PRON
ijassa-899	21	2	are	be	VERB
ijassa-899	21	3	many	many	ADJ
ijassa-899	21	4	other	other	ADJ
ijassa-899	21	5	theoretical	theoretical	ADJ
ijassa-899	21	6	properties	property	NOUN
ijassa-899	21	7	of	of	ADP
ijassa-899	21	8	the	the	DET
ijassa-899	21	9	student	student	NOUN
ijassa-899	21	10	distribution	distribution	NOUN
ijassa-899	21	11	.	.	PUNCT
ijassa-899	22	1	all	all	DET
ijassa-899	22	2	the	the	DET
ijassa-899	22	3	most	most	ADV
ijassa-899	22	4	important	important	ADJ
ijassa-899	22	5	of	of	ADP
ijassa-899	22	6	them	they	PRON
ijassa-899	22	7	used	use	VERB
ijassa-899	22	8	in	in	ADP
ijassa-899	22	9	the	the	DET
ijassa-899	22	10	paper	paper	NOUN
ijassa-899	22	11	are	be	AUX
ijassa-899	22	12	given	give	VERB
ijassa-899	22	13	in	in	ADP
ijassa-899	22	14	section	section	NOUN
ijassa-899	22	15	2	2	NUM
ijassa-899	22	16	.	.	PUNCT
ijassa-899	22	17	mixtures	mixture	NOUN
ijassa-899	22	18	of	of	ADP
ijassa-899	22	19	normal	normal	ADJ
ijassa-899	22	20	distributions	distribution	NOUN
ijassa-899	22	21	are	be	AUX
ijassa-899	22	22	often	often	ADV
ijassa-899	22	23	used	use	VERB
ijassa-899	22	24	to	to	PART
ijassa-899	22	25	describe	describe	VERB
ijassa-899	22	26	data	datum	NOUN
ijassa-899	22	27	.	.	PUNCT
ijassa-899	23	1	parameters	parameter	NOUN
ijassa-899	23	2	of	of	ADP
ijassa-899	23	3	such	such	DET
ijassa-899	23	4	a	a	DET
ijassa-899	23	5	mixture	mixture	NOUN
ijassa-899	23	6	are	be	AUX
ijassa-899	23	7	usually	usually	ADV
ijassa-899	23	8	estimated	estimate	VERB
ijassa-899	23	9	using	use	VERB
ijassa-899	23	10	the	the	DET
ijassa-899	23	11	em	em	PROPN
ijassa-899	23	12	algorithm	algorithm	NOUN
ijassa-899	23	13	[	[	X
ijassa-899	23	14	6	6	NUM
ijassa-899	23	15	]	]	PUNCT
ijassa-899	23	16	.	.	PUNCT
ijassa-899	24	1	for	for	ADP
ijassa-899	24	2	a	a	DET
ijassa-899	24	3	description	description	NOUN
ijassa-899	24	4	of	of	ADP
ijassa-899	24	5	the	the	DET
ijassa-899	24	6	em	em	PROPN
ijassa-899	24	7	algorithm	algorithm	NOUN
ijassa-899	24	8	and	and	CCONJ
ijassa-899	24	9	some	some	DET
ijassa-899	24	10	theoretical	theoretical	ADJ
ijassa-899	24	11	properties	property	NOUN
ijassa-899	24	12	of	of	ADP
ijassa-899	24	13	a	a	DET
ijassa-899	24	14	mixture	mixture	NOUN
ijassa-899	24	15	of	of	ADP
ijassa-899	24	16	distributions	distribution	NOUN
ijassa-899	24	17	,	,	PUNCT
ijassa-899	24	18	see	see	VERB
ijassa-899	24	19	section	section	NOUN
ijassa-899	24	20	3	3	NUM
ijassa-899	24	21	.	.	PUNCT
ijassa-899	25	1	if	if	SCONJ
ijassa-899	25	2	there	there	PRON
ijassa-899	25	3	are	be	VERB
ijassa-899	25	4	outliers	outlier	NOUN
ijassa-899	25	5	in	in	ADP
ijassa-899	25	6	data	datum	NOUN
ijassa-899	25	7	,	,	PUNCT
ijassa-899	25	8	it	it	PRON
ijassa-899	25	9	is	be	AUX
ijassa-899	25	10	natural	natural	ADJ
ijassa-899	25	11	to	to	PART
ijassa-899	25	12	consider	consider	VERB
ijassa-899	25	13	a	a	DET
ijassa-899	25	14	mixture	mixture	NOUN
ijassa-899	25	15	of	of	ADP
ijassa-899	25	16	student	student	NOUN
ijassa-899	25	17	distributions	distribution	NOUN
ijassa-899	25	18	.	.	PUNCT
ijassa-899	26	1	some	some	DET
ijassa-899	26	2	ideas	idea	NOUN
ijassa-899	26	3	∗corresponding	∗corresponde	VERB
ijassa-899	26	4	author	author	NOUN
ijassa-899	26	5	:	:	PUNCT
ijassa-899	26	6	volkov.na@cet-mipt.ru	volkov.na@cet-mipt.ru	ADJ
ijassa-899	26	7	student	student	NOUN
ijassa-899	26	8	mixture	mixture	NOUN
ijassa-899	26	9	and	and	CCONJ
ijassa-899	26	10	its	its	PRON
ijassa-899	26	11	machine	machine	NOUN
ijassa-899	26	12	learning	learn	VERB
ijassa-899	26	13	applications	application	NOUN
ijassa-899	26	14	to	to	ADP
ijassa-899	26	15	pvt	pvt	PROPN
ijassa-899	26	16	properties	property	NOUN
ijassa-899	26	17	99	99	NUM
ijassa-899	26	18	for	for	ADP
ijassa-899	26	19	the	the	DET
ijassa-899	26	20	parameters	parameter	NOUN
ijassa-899	26	21	estimation	estimation	NOUN
ijassa-899	26	22	of	of	ADP
ijassa-899	26	23	the	the	DET
ijassa-899	26	24	student	student	NOUN
ijassa-899	26	25	mixture	mixture	NOUN
ijassa-899	26	26	were	be	AUX
ijassa-899	26	27	described	describe	VERB
ijassa-899	26	28	in	in	ADP
ijassa-899	26	29	[	[	X
ijassa-899	26	30	7	7	NUM
ijassa-899	26	31	]	]	PUNCT
ijassa-899	26	32	,	,	PUNCT
ijassa-899	26	33	[	[	X
ijassa-899	26	34	8	8	NUM
ijassa-899	26	35	]	]	PUNCT
ijassa-899	26	36	,	,	PUNCT
ijassa-899	26	37	[	[	X
ijassa-899	26	38	9	9	NUM
ijassa-899	26	39	]	]	PUNCT
ijassa-899	26	40	and	and	CCONJ
ijassa-899	26	41	[	[	X
ijassa-899	26	42	12	12	NUM
ijassa-899	26	43	]	]	PUNCT
ijassa-899	26	44	.	.	PUNCT
ijassa-899	27	1	in	in	ADP
ijassa-899	27	2	particular	particular	ADJ
ijassa-899	27	3	,	,	PUNCT
ijassa-899	27	4	[	[	X
ijassa-899	27	5	7	7	X
ijassa-899	27	6	]	]	PUNCT
ijassa-899	27	7	describes	describe	VERB
ijassa-899	27	8	a	a	DET
ijassa-899	27	9	conditional	conditional	ADJ
ijassa-899	27	10	em	em	PRON
ijassa-899	27	11	algorithm	algorithm	NOUN
ijassa-899	27	12	.	.	PUNCT
ijassa-899	28	1	in	in	ADP
ijassa-899	28	2	this	this	DET
ijassa-899	28	3	paper	paper	NOUN
ijassa-899	28	4	,	,	PUNCT
ijassa-899	28	5	section	section	NOUN
ijassa-899	28	6	4	4	NUM
ijassa-899	28	7	provides	provide	VERB
ijassa-899	28	8	a	a	DET
ijassa-899	28	9	complete	complete	ADJ
ijassa-899	28	10	derivation	derivation	NOUN
ijassa-899	28	11	of	of	ADP
ijassa-899	28	12	the	the	DET
ijassa-899	28	13	parameter	parameter	NOUN
ijassa-899	28	14	estimation	estimation	NOUN
ijassa-899	28	15	method	method	NOUN
ijassa-899	28	16	using	use	VERB
ijassa-899	28	17	a	a	DET
ijassa-899	28	18	similar	similar	ADJ
ijassa-899	28	19	variation	variation	NOUN
ijassa-899	28	20	of	of	ADP
ijassa-899	28	21	the	the	DET
ijassa-899	28	22	em	em	PROPN
ijassa-899	28	23	algorithm	algorithm	NOUN
ijassa-899	28	24	,	,	PUNCT
ijassa-899	28	25	which	which	PRON
ijassa-899	28	26	optimizes	optimize	VERB
ijassa-899	28	27	the	the	DET
ijassa-899	28	28	variational	variational	ADJ
ijassa-899	28	29	bayesian	bayesian	NOUN
ijassa-899	28	30	inference	inference	NOUN
ijassa-899	28	31	at	at	ADP
ijassa-899	28	32	the	the	DET
ijassa-899	28	33	e	e	NOUN
ijassa-899	28	34	-	-	NOUN
ijassa-899	28	35	step	step	NOUN
ijassa-899	28	36	(	(	PUNCT
ijassa-899	28	37	see	see	VERB
ijassa-899	28	38	[	[	X
ijassa-899	28	39	6	6	NUM
ijassa-899	28	40	]	]	NUM
ijassa-899	28	41	)	)	PUNCT
ijassa-899	28	42	.	.	PUNCT
ijassa-899	29	1	this	this	DET
ijassa-899	29	2	probabilistic	probabilistic	ADJ
ijassa-899	29	3	model	model	NOUN
ijassa-899	29	4	for	for	ADP
ijassa-899	29	5	data	datum	NOUN
ijassa-899	29	6	description	description	NOUN
ijassa-899	29	7	has	have	VERB
ijassa-899	29	8	many	many	ADJ
ijassa-899	29	9	practical	practical	ADJ
ijassa-899	29	10	applications	application	NOUN
ijassa-899	29	11	,	,	PUNCT
ijassa-899	29	12	which	which	PRON
ijassa-899	29	13	allow	allow	VERB
ijassa-899	29	14	it	it	PRON
ijassa-899	29	15	to	to	PART
ijassa-899	29	16	be	be	AUX
ijassa-899	29	17	flexibly	flexibly	ADV
ijassa-899	29	18	configured	configure	VERB
ijassa-899	29	19	and	and	CCONJ
ijassa-899	29	20	conduct	conduct	VERB
ijassa-899	29	21	extensive	extensive	ADJ
ijassa-899	29	22	data	datum	NOUN
ijassa-899	29	23	analytics	analytic	NOUN
ijassa-899	29	24	.	.	PUNCT
ijassa-899	30	1	let	let	VERB
ijassa-899	30	2	us	we	PRON
ijassa-899	30	3	list	list	VERB
ijassa-899	30	4	the	the	DET
ijassa-899	30	5	applications	application	NOUN
ijassa-899	30	6	discussed	discuss	VERB
ijassa-899	30	7	in	in	ADP
ijassa-899	30	8	detail	detail	NOUN
ijassa-899	30	9	in	in	ADP
ijassa-899	30	10	section	section	NOUN
ijassa-899	30	11	5	5	NUM
ijassa-899	30	12	:	:	SYM
ijassa-899	30	13	1	1	NUM
ijassa-899	30	14	.	.	X
ijassa-899	30	15	clustering	cluster	VERB
ijassa-899	30	16	with	with	ADP
ijassa-899	30	17	a	a	DET
ijassa-899	30	18	predefined	predefine	VERB
ijassa-899	30	19	number	number	NOUN
ijassa-899	30	20	of	of	ADP
ijassa-899	30	21	clusters	cluster	NOUN
ijassa-899	30	22	.	.	PUNCT
ijassa-899	31	1	2	2	X
ijassa-899	31	2	.	.	X
ijassa-899	31	3	detecting	detect	VERB
ijassa-899	31	4	anomalies	anomaly	NOUN
ijassa-899	31	5	.	.	PUNCT
ijassa-899	32	1	3	3	X
ijassa-899	32	2	.	.	X
ijassa-899	32	3	regression	regression	NOUN
ijassa-899	32	4	to	to	PART
ijassa-899	32	5	predict	predict	VERB
ijassa-899	32	6	any	any	DET
ijassa-899	32	7	set	set	NOUN
ijassa-899	32	8	of	of	ADP
ijassa-899	32	9	real	real	ADJ
ijassa-899	32	10	features	feature	NOUN
ijassa-899	32	11	using	use	VERB
ijassa-899	32	12	any	any	DET
ijassa-899	32	13	other	other	ADJ
ijassa-899	32	14	set	set	NOUN
ijassa-899	32	15	of	of	ADP
ijassa-899	32	16	features	feature	NOUN
ijassa-899	32	17	.	.	PUNCT
ijassa-899	33	1	4	4	X
ijassa-899	33	2	.	.	X
ijassa-899	33	3	filling	fill	VERB
ijassa-899	33	4	missing	miss	VERB
ijassa-899	33	5	values	value	NOUN
ijassa-899	33	6	.	.	PUNCT
ijassa-899	34	1	this	this	DET
ijassa-899	34	2	large	large	ADJ
ijassa-899	34	3	number	number	NOUN
ijassa-899	34	4	of	of	ADP
ijassa-899	34	5	applications	application	NOUN
ijassa-899	34	6	is	be	AUX
ijassa-899	34	7	due	due	ADJ
ijassa-899	34	8	to	to	ADP
ijassa-899	34	9	the	the	DET
ijassa-899	34	10	fact	fact	NOUN
ijassa-899	34	11	that	that	SCONJ
ijassa-899	34	12	the	the	DET
ijassa-899	34	13	mixture	mixture	NOUN
ijassa-899	34	14	model	model	NOUN
ijassa-899	34	15	is	be	AUX
ijassa-899	34	16	generative	generative	ADJ
ijassa-899	34	17	,	,	PUNCT
ijassa-899	34	18	since	since	SCONJ
ijassa-899	34	19	it	it	PRON
ijassa-899	34	20	describes	describe	VERB
ijassa-899	34	21	the	the	DET
ijassa-899	34	22	joint	joint	ADJ
ijassa-899	34	23	distribution	distribution	NOUN
ijassa-899	34	24	of	of	ADP
ijassa-899	34	25	all	all	DET
ijassa-899	34	26	features	feature	NOUN
ijassa-899	34	27	.	.	PUNCT
ijassa-899	35	1	this	this	DET
ijassa-899	35	2	distribution	distribution	NOUN
ijassa-899	35	3	also	also	ADV
ijassa-899	35	4	allows	allow	VERB
ijassa-899	35	5	to	to	PART
ijassa-899	35	6	create	create	VERB
ijassa-899	35	7	artificial	artificial	ADJ
ijassa-899	35	8	data	datum	NOUN
ijassa-899	35	9	.	.	PUNCT
ijassa-899	36	1	the	the	DET
ijassa-899	36	2	model	model	NOUN
ijassa-899	36	3	of	of	ADP
ijassa-899	36	4	a	a	DET
ijassa-899	36	5	mixture	mixture	NOUN
ijassa-899	36	6	of	of	ADP
ijassa-899	36	7	distributions	distribution	NOUN
ijassa-899	36	8	can	can	AUX
ijassa-899	36	9	be	be	AUX
ijassa-899	36	10	recommended	recommend	VERB
ijassa-899	36	11	to	to	PART
ijassa-899	36	12	solve	solve	VERB
ijassa-899	36	13	problems	problem	NOUN
ijassa-899	36	14	with	with	ADP
ijassa-899	36	15	expected	expect	VERB
ijassa-899	36	16	continuous	continuous	ADJ
ijassa-899	36	17	dependence	dependence	NOUN
ijassa-899	36	18	of	of	ADP
ijassa-899	36	19	features	feature	NOUN
ijassa-899	36	20	between	between	ADP
ijassa-899	36	21	each	each	DET
ijassa-899	36	22	other	other	ADJ
ijassa-899	36	23	,	,	PUNCT
ijassa-899	36	24	for	for	ADP
ijassa-899	36	25	example	example	NOUN
ijassa-899	36	26	,	,	PUNCT
ijassa-899	36	27	physical	physical	ADJ
ijassa-899	36	28	problems	problem	NOUN
ijassa-899	36	29	.	.	PUNCT
ijassa-899	37	1	in	in	ADP
ijassa-899	37	2	sections	section	NOUN
ijassa-899	37	3	6	6	NUM
ijassa-899	37	4	and	and	CCONJ
ijassa-899	37	5	7	7	NUM
ijassa-899	37	6	,	,	PUNCT
ijassa-899	37	7	we	we	PRON
ijassa-899	37	8	apply	apply	VERB
ijassa-899	37	9	our	our	PRON
ijassa-899	37	10	model	model	NOUN
ijassa-899	37	11	to	to	ADP
ijassa-899	37	12	pvt	pvt	PROPN
ijassa-899	37	13	properties	property	NOUN
ijassa-899	37	14	of	of	ADP
ijassa-899	37	15	reservoir	reservoir	NOUN
ijassa-899	37	16	fluids	fluid	NOUN
ijassa-899	37	17	,	,	PUNCT
ijassa-899	37	18	where	where	SCONJ
ijassa-899	37	19	it	it	PRON
ijassa-899	37	20	shows	show	VERB
ijassa-899	37	21	high	high	ADJ
ijassa-899	37	22	quality	quality	NOUN
ijassa-899	37	23	relative	relative	ADJ
ijassa-899	37	24	to	to	ADP
ijassa-899	37	25	widely	widely	ADV
ijassa-899	37	26	known	know	VERB
ijassa-899	37	27	machine	machine	NOUN
ijassa-899	37	28	learning	learning	NOUN
ijassa-899	37	29	models	model	NOUN
ijassa-899	37	30	.	.	PUNCT
ijassa-899	38	1	it	it	PRON
ijassa-899	38	2	should	should	AUX
ijassa-899	38	3	be	be	AUX
ijassa-899	38	4	remarked	remark	VERB
ijassa-899	38	5	that	that	SCONJ
ijassa-899	38	6	,	,	PUNCT
ijassa-899	38	7	the	the	DET
ijassa-899	38	8	obtained	obtain	VERB
ijassa-899	38	9	experimental	experimental	ADJ
ijassa-899	38	10	results	result	NOUN
ijassa-899	38	11	do	do	AUX
ijassa-899	38	12	not	not	PART
ijassa-899	38	13	contradict	contradict	VERB
ijassa-899	38	14	physical	physical	ADJ
ijassa-899	38	15	laws	law	NOUN
ijassa-899	38	16	,	,	PUNCT
ijassa-899	38	17	unlike	unlike	ADP
ijassa-899	38	18	outcomes	outcome	NOUN
ijassa-899	38	19	of	of	ADP
ijassa-899	38	20	many	many	ADJ
ijassa-899	38	21	machine	machine	NOUN
ijassa-899	38	22	learning	learning	NOUN
ijassa-899	38	23	methods	method	NOUN
ijassa-899	38	24	,	,	PUNCT
ijassa-899	38	25	in	in	ADP
ijassa-899	38	26	particular	particular	ADJ
ijassa-899	38	27	those	those	PRON
ijassa-899	38	28	based	base	VERB
ijassa-899	38	29	on	on	ADP
ijassa-899	38	30	decision	decision	NOUN
ijassa-899	38	31	trees	tree	NOUN
ijassa-899	38	32	.	.	PUNCT
ijassa-899	39	1	2	2	X
ijassa-899	39	2	.	.	X
ijassa-899	39	3	distributions	distribution	NOUN
ijassa-899	39	4	and	and	CCONJ
ijassa-899	39	5	their	their	PRON
ijassa-899	39	6	properties	property	NOUN
ijassa-899	39	7	this	this	DET
ijassa-899	39	8	section	section	NOUN
ijassa-899	39	9	provides	provide	VERB
ijassa-899	39	10	definitions	definition	NOUN
ijassa-899	39	11	and	and	CCONJ
ijassa-899	39	12	some	some	DET
ijassa-899	39	13	basic	basic	ADJ
ijassa-899	39	14	properties	property	NOUN
ijassa-899	39	15	of	of	ADP
ijassa-899	39	16	the	the	DET
ijassa-899	39	17	normal	normal	ADJ
ijassa-899	39	18	and	and	CCONJ
ijassa-899	39	19	student	student	NOUN
ijassa-899	39	20	distributions	distribution	NOUN
ijassa-899	39	21	that	that	PRON
ijassa-899	39	22	are	be	AUX
ijassa-899	39	23	in	in	ADP
ijassa-899	39	24	the	the	DET
ijassa-899	39	25	basis	basis	NOUN
ijassa-899	39	26	of	of	ADP
ijassa-899	39	27	the	the	DET
ijassa-899	39	28	developed	develop	VERB
ijassa-899	39	29	model	model	NOUN
ijassa-899	39	30	.	.	PUNCT
ijassa-899	40	1	the	the	DET
ijassa-899	40	2	properties	property	NOUN
ijassa-899	40	3	of	of	ADP
ijassa-899	40	4	the	the	DET
ijassa-899	40	5	gamma	gamma	NOUN
ijassa-899	40	6	distribution	distribution	NOUN
ijassa-899	40	7	are	be	AUX
ijassa-899	40	8	also	also	ADV
ijassa-899	40	9	given	give	VERB
ijassa-899	40	10	as	as	SCONJ
ijassa-899	40	11	they	they	PRON
ijassa-899	40	12	are	be	AUX
ijassa-899	40	13	to	to	PART
ijassa-899	40	14	represent	represent	VERB
ijassa-899	40	15	student	student	NOUN
ijassa-899	40	16	random	random	ADJ
ijassa-899	40	17	vector	vector	NOUN
ijassa-899	40	18	in	in	ADP
ijassa-899	40	19	a	a	DET
ijassa-899	40	20	convenient	convenient	ADJ
ijassa-899	40	21	form	form	NOUN
ijassa-899	40	22	.	.	PUNCT
ijassa-899	41	1	all	all	DET
ijassa-899	41	2	statements	statement	NOUN
ijassa-899	41	3	in	in	ADP
ijassa-899	41	4	this	this	DET
ijassa-899	41	5	section	section	NOUN
ijassa-899	41	6	can	can	AUX
ijassa-899	41	7	be	be	AUX
ijassa-899	41	8	found	find	VERB
ijassa-899	41	9	,	,	PUNCT
ijassa-899	41	10	for	for	ADP
ijassa-899	41	11	example	example	NOUN
ijassa-899	41	12	,	,	PUNCT
ijassa-899	41	13	in	in	ADP
ijassa-899	41	14	the	the	DET
ijassa-899	41	15	literature	literature	NOUN
ijassa-899	42	1	[	[	X
ijassa-899	42	2	1	1	NUM
ijassa-899	42	3	]	]	PUNCT
ijassa-899	42	4	,	,	PUNCT
ijassa-899	43	1	[	[	X
ijassa-899	43	2	2	2	NUM
ijassa-899	43	3	]	]	PUNCT
ijassa-899	43	4	,	,	PUNCT
ijassa-899	43	5	[	[	X
ijassa-899	43	6	3	3	NUM
ijassa-899	43	7	]	]	PUNCT
ijassa-899	43	8	,	,	PUNCT
ijassa-899	43	9	[	[	X
ijassa-899	43	10	4	4	NUM
ijassa-899	43	11	]	]	PUNCT
ijassa-899	43	12	,	,	PUNCT
ijassa-899	43	13	[	[	X
ijassa-899	43	14	5	5	NUM
ijassa-899	43	15	]	]	PUNCT
ijassa-899	43	16	,	,	PUNCT
ijassa-899	43	17	[	[	X
ijassa-899	43	18	6	6	NUM
ijassa-899	43	19	]	]	PUNCT
ijassa-899	43	20	.	.	PUNCT
ijassa-899	44	1	2.1	2.1	NUM
ijassa-899	44	2	.	.	PUNCT
ijassa-899	44	3	normal	normal	ADJ
ijassa-899	44	4	distribution	distribution	NOUN
ijassa-899	44	5	the	the	DET
ijassa-899	44	6	density	density	NOUN
ijassa-899	44	7	of	of	ADP
ijassa-899	44	8	the	the	DET
ijassa-899	44	9	multidimensional	multidimensional	ADJ
ijassa-899	44	10	normal	normal	ADJ
ijassa-899	44	11	distribution	distribution	NOUN
ijassa-899	44	12	centered	center	VERB
ijassa-899	44	13	at	at	ADP
ijassa-899	44	14	a	a	DET
ijassa-899	44	15	point	point	NOUN
ijassa-899	44	16	µ	µ	X
ijassa-899	44	17	∈	∈	PROPN
ijassa-899	44	18	rd	rd	NOUN
ijassa-899	44	19	and	and	CCONJ
ijassa-899	44	20	a	a	DET
ijassa-899	44	21	symmetric	symmetric	ADJ
ijassa-899	44	22	positive	positive	ADJ
ijassa-899	44	23	definite	definite	ADJ
ijassa-899	44	24	covariance	covariance	NOUN
ijassa-899	44	25	matrix	matrix	NOUN
ijassa-899	44	26	σ	σ	NOUN
ijassa-899	44	27	equals	equal	VERB
ijassa-899	44	28	q(x|µ,σ	q(x|µ,σ	NOUN
ijassa-899	44	29	)	)	PUNCT
ijassa-899	45	1	=	=	SYM
ijassa-899	45	2	1	1	NUM
ijassa-899	45	3	(	(	PUNCT
ijassa-899	45	4	2π)d/2	2π)d/2	NUM
ijassa-899	45	5	√	√	PROPN
ijassa-899	45	6	det	det	PROPN
ijassa-899	45	7	σ	σ	PROPN
ijassa-899	45	8	exp	exp	PROPN
ijassa-899	45	9	(	(	PUNCT
ijassa-899	45	10	−1	−1	NOUN
ijassa-899	45	11	2	2	NUM
ijassa-899	45	12	(	(	PUNCT
ijassa-899	45	13	x−	x−	PROPN
ijassa-899	45	14	µ)tς−1(x−	µ)tς−1(x−	PUNCT
ijassa-899	45	15	µ	µ	PROPN
ijassa-899	45	16	)	)	PUNCT
ijassa-899	45	17	)	)	PUNCT
ijassa-899	45	18	,	,	PUNCT
ijassa-899	45	19	where	where	SCONJ
ijassa-899	45	20	detς	detς	PROPN
ijassa-899	45	21	means	mean	VERB
ijassa-899	45	22	the	the	DET
ijassa-899	45	23	determinant	determinant	NOUN
ijassa-899	45	24	of	of	ADP
ijassa-899	45	25	matrix	matrix	NOUN
ijassa-899	45	26	σ	σ	NOUN
ijassa-899	45	27	.	.	PUNCT
ijassa-899	46	1	we	we	PRON
ijassa-899	46	2	will	will	AUX
ijassa-899	46	3	use	use	VERB
ijassa-899	46	4	this	this	DET
ijassa-899	46	5	notation	notation	NOUN
ijassa-899	46	6	for	for	ADP
ijassa-899	46	7	the	the	DET
ijassa-899	46	8	normal	normal	ADJ
ijassa-899	46	9	distribution	distribution	NOUN
ijassa-899	46	10	density	density	NOUN
ijassa-899	46	11	throughout	throughout	ADV
ijassa-899	46	12	.	.	PUNCT
ijassa-899	47	1	let	let	VERB
ijassa-899	47	2	matrix	matrix	NOUN
ijassa-899	47	3	b	b	NOUN
ijassa-899	47	4	be	be	AUX
ijassa-899	47	5	the	the	DET
ijassa-899	47	6	root	root	NOUN
ijassa-899	47	7	of	of	ADP
ijassa-899	47	8	the	the	DET
ijassa-899	47	9	matrix	matrix	NOUN
ijassa-899	47	10	σ	σ	NOUN
ijassa-899	47	11	that	that	PRON
ijassa-899	47	12	is	be	AUX
ijassa-899	47	13	satisfying	satisfy	VERB
ijassa-899	47	14	the	the	DET
ijassa-899	47	15	condition	condition	NOUN
ijassa-899	47	16	bbt	bbt	PROPN
ijassa-899	47	17	=	=	PROPN
ijassa-899	47	18	σ	σ	PROPN
ijassa-899	47	19	.	.	PUNCT
ijassa-899	48	1	then	then	ADV
ijassa-899	48	2	,	,	PUNCT
ijassa-899	48	3	if	if	SCONJ
ijassa-899	48	4	ξ	ξ	PROPN
ijassa-899	48	5	has	have	VERB
ijassa-899	48	6	distribution	distribution	NOUN
ijassa-899	48	7	n	n	CCONJ
ijassa-899	48	8	(	(	PUNCT
ijassa-899	48	9	0	0	NUM
ijassa-899	48	10	,	,	PUNCT
ijassa-899	48	11	i	i	PROPN
ijassa-899	48	12	d	d	PROPN
ijassa-899	48	13	)	)	PUNCT
ijassa-899	48	14	,	,	PUNCT
ijassa-899	48	15	where	where	SCONJ
ijassa-899	48	16	i	i	PRON
ijassa-899	48	17	d	d	PROPN
ijassa-899	48	18	is	be	AUX
ijassa-899	48	19	identity	identity	NOUN
ijassa-899	48	20	matrix	matrix	NOUN
ijassa-899	48	21	of	of	ADP
ijassa-899	48	22	dimension	dimension	NOUN
ijassa-899	48	23	d	d	PROPN
ijassa-899	48	24	,	,	PUNCT
ijassa-899	48	25	then	then	ADV
ijassa-899	48	26	η	η	PROPN
ijassa-899	48	27	=	=	PRON
ijassa-899	48	28	µ+bξ	µ+bξ	PRON
ijassa-899	48	29	has	have	VERB
ijassa-899	48	30	distribution	distribution	NOUN
ijassa-899	48	31	n	n	CCONJ
ijassa-899	48	32	(	(	PUNCT
ijassa-899	48	33	µ,σ	µ,σ	PROPN
ijassa-899	48	34	)	)	PUNCT
ijassa-899	48	35	.	.	PUNCT
ijassa-899	49	1	2.2	2.2	NUM
ijassa-899	49	2	.	.	PUNCT
ijassa-899	50	1	gamma	gamma	NOUN
ijassa-899	50	2	distribution	distribution	NOUN
ijassa-899	50	3	the	the	DET
ijassa-899	50	4	gamma	gamma	NOUN
ijassa-899	50	5	distribution	distribution	NOUN
ijassa-899	50	6	density	density	NOUN
ijassa-899	50	7	γ(α	γ(α	PROPN
ijassa-899	50	8	,	,	PUNCT
ijassa-899	50	9	β	β	NOUN
ijassa-899	50	10	)	)	PUNCT
ijassa-899	50	11	is	be	AUX
ijassa-899	50	12	γ(x|α	γ(x|α	PROPN
ijassa-899	50	13	,	,	PUNCT
ijassa-899	50	14	β	β	X
ijassa-899	50	15	)	)	PUNCT
ijassa-899	50	16	=	=	SYM
ijassa-899	51	1	αβ	αβ	DET
ijassa-899	51	2	γ(β	γ(β	PROPN
ijassa-899	51	3	)	)	PUNCT
ijassa-899	51	4	xβ−1e−αxi{x	xβ−1e−αxi{x	PROPN
ijassa-899	51	5	>	>	X
ijassa-899	51	6	0	0	NUM
ijassa-899	51	7	}	}	PUNCT
ijassa-899	51	8	.	.	PUNCT
ijassa-899	52	1	we	we	PRON
ijassa-899	52	2	will	will	AUX
ijassa-899	52	3	use	use	VERB
ijassa-899	52	4	this	this	DET
ijassa-899	52	5	notation	notation	NOUN
ijassa-899	52	6	for	for	ADP
ijassa-899	52	7	the	the	DET
ijassa-899	52	8	density	density	NOUN
ijassa-899	52	9	of	of	ADP
ijassa-899	52	10	gamma	gamma	NOUN
ijassa-899	52	11	distribution	distribution	NOUN
ijassa-899	52	12	throughout	throughout	ADP
ijassa-899	52	13	.	.	PUNCT
ijassa-899	53	1	let	let	VERB
ijassa-899	53	2	ξ	ξ	X
ijassa-899	53	3	∼	∼	NOUN
ijassa-899	53	4	γ(α	γ(α	NOUN
ijassa-899	53	5	,	,	PUNCT
ijassa-899	53	6	β	β	NOUN
ijassa-899	53	7	)	)	PUNCT
ijassa-899	53	8	.	.	PUNCT
ijassa-899	54	1	it	it	PRON
ijassa-899	54	2	is	be	AUX
ijassa-899	54	3	not	not	PART
ijassa-899	54	4	difficult	difficult	ADJ
ijassa-899	54	5	to	to	PART
ijassa-899	54	6	make	make	VERB
ijassa-899	54	7	sure	sure	ADJ
ijassa-899	54	8	that	that	SCONJ
ijassa-899	54	9	eξ	eξ	X
ijassa-899	54	10	=	=	PUNCT
ijassa-899	54	11	β	β	X
ijassa-899	54	12	/	/	SYM
ijassa-899	54	13	α	α	PROPN
ijassa-899	55	1	[	[	X
ijassa-899	55	2	1	1	NUM
ijassa-899	55	3	]	]	PUNCT
ijassa-899	55	4	,	,	PUNCT
ijassa-899	55	5	e	e	PROPN
ijassa-899	55	6	ln	ln	ADP
ijassa-899	55	7	ξ	ξ	PROPN
ijassa-899	55	8	=	=	PUNCT
ijassa-899	55	9	ψ(β)−	ψ(β)−	PROPN
ijassa-899	55	10	lnα	lnα	PROPN
ijassa-899	55	11	,	,	PUNCT
ijassa-899	55	12	where	where	SCONJ
ijassa-899	55	13	ψ(x	ψ(x	NOUN
ijassa-899	55	14	)	)	PUNCT
ijassa-899	56	1	=	=	PUNCT
ijassa-899	57	1	d	d	X
ijassa-899	57	2	ln	ln	ADJ
ijassa-899	57	3	γ(x	γ(x	PROPN
ijassa-899	57	4	)	)	PUNCT
ijassa-899	57	5	dx	dx	PROPN
ijassa-899	57	6	is	be	AUX
ijassa-899	57	7	digamma	digamma	PROPN
ijassa-899	57	8	function	function	NOUN
ijassa-899	57	9	.	.	PUNCT
ijassa-899	58	1	if	if	SCONJ
ijassa-899	58	2	β	β	X
ijassa-899	58	3	>	>	X
ijassa-899	58	4	1	1	NUM
ijassa-899	58	5	we	we	PRON
ijassa-899	58	6	also	also	ADV
ijassa-899	58	7	get	get	VERB
ijassa-899	58	8	eξ−1	eξ−1	PROPN
ijassa-899	58	9	=	=	SYM
ijassa-899	58	10	α	α	PROPN
ijassa-899	58	11	β−1	β−1	PUNCT
ijassa-899	58	12	.	.	PUNCT
ijassa-899	59	1	copyright	copyright	NOUN
ijassa-899	59	2	©	©	PROPN
ijassa-899	59	3	2020	2020	NUM
ijassa-899	59	4	assa	assa	NOUN
ijassa-899	59	5	.	.	PUNCT
ijassa-899	60	1	adv	adv	PROPN
ijassa-899	60	2	syst	syst	PROPN
ijassa-899	60	3	sci	sci	PROPN
ijassa-899	60	4	appl	appl	PROPN
ijassa-899	60	5	(	(	PUNCT
ijassa-899	60	6	2020	2020	NUM
ijassa-899	60	7	)	)	PUNCT
ijassa-899	60	8	100	100	NUM
ijassa-899	60	9	n.a	n.a	PROPN
ijassa-899	60	10	.	.	PROPN
ijassa-899	60	11	volkov	volkov	PROPN
ijassa-899	60	12	,	,	PUNCT
ijassa-899	60	13	e.yu	e.yu	PROPN
ijassa-899	60	14	.	.	PROPN
ijassa-899	60	15	dakhova	dakhova	PROPN
ijassa-899	60	16	,	,	PUNCT
ijassa-899	60	17	s.a	s.a	PROPN
ijassa-899	60	18	.	.	PROPN
ijassa-899	60	19	budennyy	budennyy	PROPN
ijassa-899	60	20	,	,	PUNCT
ijassa-899	60	21	a.m.	a.m.	PROPN
ijassa-899	60	22	andrianova	andrianova	VERB
ijassa-899	61	1	2.3	2.3	NUM
ijassa-899	61	2	.	.	PUNCT
ijassa-899	62	1	student	student	NOUN
ijassa-899	62	2	distribution	distribution	NOUN
ijassa-899	62	3	the	the	DET
ijassa-899	62	4	student	student	NOUN
ijassa-899	62	5	distribution	distribution	NOUN
ijassa-899	62	6	has	have	VERB
ijassa-899	62	7	parameter	parameter	NOUN
ijassa-899	62	8	ν	ν	NOUN
ijassa-899	62	9	indicating	indicate	VERB
ijassa-899	62	10	the	the	DET
ijassa-899	62	11	number	number	NOUN
ijassa-899	62	12	of	of	ADP
ijassa-899	62	13	degrees	degree	NOUN
ijassa-899	62	14	of	of	ADP
ijassa-899	62	15	freedom	freedom	NOUN
ijassa-899	62	16	.	.	PUNCT
ijassa-899	63	1	the	the	PRON
ijassa-899	63	2	less	less	ADJ
ijassa-899	63	3	the	the	DET
ijassa-899	63	4	number	number	NOUN
ijassa-899	63	5	of	of	ADP
ijassa-899	63	6	degrees	degree	NOUN
ijassa-899	63	7	of	of	ADP
ijassa-899	63	8	freedom	freedom	NOUN
ijassa-899	63	9	,	,	PUNCT
ijassa-899	63	10	the	the	DET
ijassa-899	63	11	heavier	heavy	ADJ
ijassa-899	63	12	student	student	NOUN
ijassa-899	63	13	distribution	distribution	NOUN
ijassa-899	63	14	tails	tail	NOUN
ijassa-899	63	15	,	,	PUNCT
ijassa-899	63	16	and	and	CCONJ
ijassa-899	63	17	the	the	DET
ijassa-899	63	18	more	more	ADV
ijassa-899	63	19	resistant	resistant	ADJ
ijassa-899	63	20	it	it	PRON
ijassa-899	63	21	is	be	AUX
ijassa-899	63	22	to	to	ADP
ijassa-899	63	23	outliers	outlier	NOUN
ijassa-899	63	24	.	.	PUNCT
ijassa-899	64	1	moreover	moreover	ADV
ijassa-899	64	2	,	,	PUNCT
ijassa-899	64	3	the	the	DET
ijassa-899	64	4	student	student	NOUN
ijassa-899	64	5	distribution	distribution	NOUN
ijassa-899	64	6	converges	converge	VERB
ijassa-899	64	7	in	in	ADP
ijassa-899	64	8	distribution	distribution	NOUN
ijassa-899	64	9	to	to	ADP
ijassa-899	64	10	the	the	DET
ijassa-899	64	11	normal	normal	ADJ
ijassa-899	64	12	distribution	distribution	NOUN
ijassa-899	64	13	when	when	SCONJ
ijassa-899	64	14	the	the	DET
ijassa-899	64	15	nu	nu	PROPN
ijassa-899	64	16	converges	converge	VERB
ijassa-899	64	17	to	to	ADP
ijassa-899	64	18	infinity	infinity	NOUN
ijassa-899	64	19	.	.	PUNCT
ijassa-899	65	1	we	we	PRON
ijassa-899	65	2	denote	denote	VERB
ijassa-899	65	3	multidimensional	multidimensional	ADJ
ijassa-899	65	4	student	student	NOUN
ijassa-899	65	5	distribution	distribution	NOUN
ijassa-899	65	6	with	with	ADP
ijassa-899	65	7	ν	ν	PROPN
ijassa-899	65	8	>	>	SYM
ijassa-899	65	9	0	0	NUM
ijassa-899	65	10	degrees	degree	NOUN
ijassa-899	65	11	of	of	ADP
ijassa-899	65	12	freedom	freedom	NOUN
ijassa-899	65	13	,	,	PUNCT
ijassa-899	65	14	with	with	ADP
ijassa-899	65	15	mean	mean	PROPN
ijassa-899	65	16	vector	vector	PROPN
ijassa-899	65	17	µ	µ	PROPN
ijassa-899	65	18	∈	∈	PROPN
ijassa-899	65	19	rd	rd	NOUN
ijassa-899	65	20	and	and	CCONJ
ijassa-899	65	21	symmetric	symmetric	ADJ
ijassa-899	65	22	positive	positive	ADJ
ijassa-899	65	23	semi	semi	ADJ
ijassa-899	65	24	-	-	ADJ
ijassa-899	65	25	definite	definite	ADJ
ijassa-899	65	26	and	and	CCONJ
ijassa-899	65	27	non	non	ADJ
ijassa-899	65	28	-	-	ADJ
ijassa-899	65	29	degenerate	degenerate	ADJ
ijassa-899	65	30	scale	scale	NOUN
ijassa-899	65	31	matrix	matrix	NOUN
ijassa-899	65	32	σ	σ	NOUN
ijassa-899	65	33	by	by	ADP
ijassa-899	65	34	tν(µ,σ	tν(µ,σ	NUM
ijassa-899	65	35	)	)	PUNCT
ijassa-899	65	36	.	.	PUNCT
ijassa-899	66	1	the	the	DET
ijassa-899	66	2	density	density	NOUN
ijassa-899	66	3	of	of	ADP
ijassa-899	66	4	this	this	DET
ijassa-899	66	5	distribution	distribution	NOUN
ijassa-899	66	6	in	in	ADP
ijassa-899	66	7	x	x	PART
ijassa-899	66	8	∈	∈	PROPN
ijassa-899	66	9	rd	rd	NOUN
ijassa-899	66	10	equals	equal	VERB
ijassa-899	66	11	p(x|µ,σ	p(x|µ,σ	NOUN
ijassa-899	66	12	,	,	PUNCT
ijassa-899	66	13	ν	ν	NOUN
ijassa-899	66	14	)	)	PUNCT
ijassa-899	66	15	=	=	SYM
ijassa-899	66	16	γ	γ	X
ijassa-899	66	17	(	(	PUNCT
ijassa-899	66	18	ν+d	ν+d	NUM
ijassa-899	66	19	2	2	NUM
ijassa-899	66	20	)	)	PUNCT
ijassa-899	66	21	γ(ν/2)νd/2πd/2|σ|1/2	γ(ν/2)νd/2πd/2|σ|1/2	NOUN
ijassa-899	66	22	[	[	PUNCT
ijassa-899	66	23	1	1	NUM
ijassa-899	66	24	+	+	SYM
ijassa-899	66	25	1	1	NUM
ijassa-899	66	26	ν	ν	NOUN
ijassa-899	66	27	(	(	PUNCT
ijassa-899	66	28	x−	x−	PROPN
ijassa-899	66	29	µ)tς−1(x−	µ)tς−1(x−	PUNCT
ijassa-899	66	30	µ	µ	PROPN
ijassa-899	66	31	)	)	PUNCT
ijassa-899	66	32	]	]	PUNCT
ijassa-899	66	33	−	−	PROPN
ijassa-899	66	34	ν+d	ν+d	NUM
ijassa-899	66	35	2	2	NUM
ijassa-899	66	36	.	.	PUNCT
ijassa-899	67	1	the	the	DET
ijassa-899	67	2	mathematical	mathematical	ADJ
ijassa-899	67	3	expectation	expectation	NOUN
ijassa-899	67	4	and	and	CCONJ
ijassa-899	67	5	the	the	DET
ijassa-899	67	6	covariance	covariance	NOUN
ijassa-899	67	7	matrix	matrix	NOUN
ijassa-899	67	8	of	of	ADP
ijassa-899	67	9	the	the	DET
ijassa-899	67	10	distribution	distribution	NOUN
ijassa-899	67	11	tν(µ,σ	tν(µ,σ	NUM
ijassa-899	67	12	)	)	PUNCT
ijassa-899	67	13	equal	equal	ADJ
ijassa-899	67	14	ex	ex	NOUN
ijassa-899	67	15	=	=	NOUN
ijassa-899	67	16	µ	µ	X
ijassa-899	67	17	if	if	SCONJ
ijassa-899	67	18	ν	ν	NOUN
ijassa-899	67	19	>	>	X
ijassa-899	67	20	1	1	NUM
ijassa-899	67	21	and	and	CCONJ
ijassa-899	67	22	varx	varx	NOUN
ijassa-899	67	23	=	=	PUNCT
ijassa-899	67	24	ν	ν	X
ijassa-899	67	25	ν−2	ν−2	PROPN
ijassa-899	67	26	σ	σ	X
ijassa-899	67	27	if	if	SCONJ
ijassa-899	67	28	ν	ν	X
ijassa-899	67	29	>	>	X
ijassa-899	67	30	2	2	NUM
ijassa-899	67	31	respectively	respectively	ADV
ijassa-899	67	32	.	.	PUNCT
ijassa-899	68	1	claim	claim	VERB
ijassa-899	68	2	2.1	2.1	NUM
ijassa-899	68	3	:	:	PUNCT
ijassa-899	69	1	[	[	X
ijassa-899	69	2	5	5	NUM
ijassa-899	69	3	]	]	PUNCT
ijassa-899	69	4	let	let	VERB
ijassa-899	69	5	random	random	ADJ
ijassa-899	69	6	vector	vector	NOUN
ijassa-899	69	7	ξ	ξ	PROPN
ijassa-899	69	8	and	and	CCONJ
ijassa-899	69	9	random	random	ADJ
ijassa-899	69	10	variable	variable	ADJ
ijassa-899	69	11	η	η	PROPN
ijassa-899	69	12	be	be	AUX
ijassa-899	69	13	independent	independent	ADJ
ijassa-899	69	14	and	and	CCONJ
ijassa-899	69	15	have	have	VERB
ijassa-899	69	16	distributions	distribution	NOUN
ijassa-899	69	17	n	n	X
ijassa-899	69	18	(	(	PUNCT
ijassa-899	69	19	0,σ	0,σ	PROPN
ijassa-899	69	20	)	)	PUNCT
ijassa-899	69	21	and	and	CCONJ
ijassa-899	69	22	γ(ν/2	γ(ν/2	PROPN
ijassa-899	69	23	,	,	PUNCT
ijassa-899	69	24	ν/2	ν/2	NUM
ijassa-899	69	25	)	)	PUNCT
ijassa-899	69	26	respectively	respectively	ADV
ijassa-899	69	27	.	.	PUNCT
ijassa-899	70	1	let	let	VERB
ijassa-899	70	2	µ	µ	PRON
ijassa-899	70	3	∈	∈	PROPN
ijassa-899	70	4	rd	rd	NOUN
ijassa-899	70	5	be	be	AUX
ijassa-899	70	6	a	a	DET
ijassa-899	70	7	fixed	fix	VERB
ijassa-899	70	8	vector	vector	NOUN
ijassa-899	70	9	.	.	PUNCT
ijassa-899	71	1	than	than	ADP
ijassa-899	71	2	random	random	ADJ
ijassa-899	71	3	vector	vector	NOUN
ijassa-899	71	4	x	x	NOUN
ijassa-899	71	5	=	=	NOUN
ijassa-899	71	6	µ+	µ+	PUNCT
ijassa-899	71	7	ξ/	ξ/	ADJ
ijassa-899	71	8	√	√	NUM
ijassa-899	71	9	η	η	PROPN
ijassa-899	71	10	has	have	VERB
ijassa-899	71	11	student	student	NOUN
ijassa-899	71	12	distribution	distribution	NOUN
ijassa-899	71	13	with	with	ADP
ijassa-899	71	14	ν	ν	X
ijassa-899	71	15	degrees	degree	NOUN
ijassa-899	71	16	of	of	ADP
ijassa-899	71	17	freedom	freedom	NOUN
ijassa-899	71	18	,	,	PUNCT
ijassa-899	71	19	mean	mean	VERB
ijassa-899	71	20	vector	vector	NOUN
ijassa-899	71	21	µ	µ	NOUN
ijassa-899	71	22	and	and	CCONJ
ijassa-899	71	23	scale	scale	NOUN
ijassa-899	71	24	matrix	matrix	NOUN
ijassa-899	71	25	σ	σ	NOUN
ijassa-899	71	26	.	.	PUNCT
ijassa-899	72	1	the	the	DET
ijassa-899	72	2	student	student	NOUN
ijassa-899	72	3	distribution	distribution	NOUN
ijassa-899	72	4	density	density	NOUN
ijassa-899	72	5	can	can	AUX
ijassa-899	72	6	be	be	AUX
ijassa-899	72	7	represented	represent	VERB
ijassa-899	72	8	in	in	ADP
ijassa-899	72	9	an	an	DET
ijassa-899	72	10	integral	integral	ADJ
ijassa-899	72	11	form	form	NOUN
ijassa-899	72	12	p(x|µ,σ	p(x|µ,σ	NOUN
ijassa-899	72	13	,	,	PUNCT
ijassa-899	72	14	ν	ν	NOUN
ijassa-899	72	15	)	)	PUNCT
ijassa-899	72	16	=	=	PUNCT
ijassa-899	73	1	+	+	NOUN
ijassa-899	73	2	∞∫	∞∫	NOUN
ijassa-899	73	3	0	0	NUM
ijassa-899	73	4	q(x|µ,σ	q(x|µ,σ	PROPN
ijassa-899	73	5	/	/	SYM
ijassa-899	73	6	y)γ(y|ν/2	y)γ(y|ν/2	NOUN
ijassa-899	73	7	,	,	PUNCT
ijassa-899	73	8	ν/2)dy	ν/2)dy	X
ijassa-899	73	9	.	.	PUNCT
ijassa-899	74	1	2.4	2.4	NUM
ijassa-899	74	2	.	.	PUNCT
ijassa-899	75	1	marginal	marginal	ADJ
ijassa-899	75	2	distributions	distribution	NOUN
ijassa-899	75	3	let	let	VERB
ijassa-899	75	4	a	a	DET
ijassa-899	75	5	,	,	PUNCT
ijassa-899	75	6	b	b	PROPN
ijassa-899	75	7	be	be	AUX
ijassa-899	75	8	disjoint	disjoint	NOUN
ijassa-899	75	9	sets	set	NOUN
ijassa-899	75	10	of	of	ADP
ijassa-899	75	11	indexes	index	NOUN
ijassa-899	75	12	,	,	PUNCT
ijassa-899	75	13	and	and	CCONJ
ijassa-899	75	14	a	a	DET
ijassa-899	75	15	t	t	NOUN
ijassa-899	75	16	b	b	X
ijassa-899	75	17	=	=	PUNCT
ijassa-899	75	18	{	{	PUNCT
ijassa-899	75	19	1	1	NUM
ijassa-899	75	20	,	,	PUNCT
ijassa-899	75	21	...	...	PUNCT
ijassa-899	75	22	,	,	PUNCT
ijassa-899	75	23	d	d	NOUN
ijassa-899	75	24	}	}	PUNCT
ijassa-899	75	25	.	.	PUNCT
ijassa-899	76	1	without	without	ADP
ijassa-899	76	2	any	any	DET
ijassa-899	76	3	loss	loss	NOUN
ijassa-899	76	4	of	of	ADP
ijassa-899	76	5	generality	generality	NOUN
ijassa-899	76	6	,	,	PUNCT
ijassa-899	76	7	we	we	PRON
ijassa-899	76	8	set	set	VERB
ijassa-899	76	9	a	a	PRON
ijassa-899	76	10	=	=	X
ijassa-899	76	11	{	{	PUNCT
ijassa-899	76	12	1	1	NUM
ijassa-899	76	13	,	,	PUNCT
ijassa-899	76	14	...	...	PUNCT
ijassa-899	76	15	,	,	PUNCT
ijassa-899	76	16	da	da	ADJ
ijassa-899	76	17	}	}	PUNCT
ijassa-899	76	18	,	,	PUNCT
ijassa-899	76	19	b	b	X
ijassa-899	76	20	=	=	PRON
ijassa-899	76	21	{	{	PUNCT
ijassa-899	76	22	da	da	NOUN
ijassa-899	76	23	+	+	NUM
ijassa-899	76	24	1	1	NUM
ijassa-899	76	25	,	,	PUNCT
ijassa-899	76	26	...	...	PUNCT
ijassa-899	76	27	,	,	PUNCT
ijassa-899	76	28	d	d	X
ijassa-899	76	29	}	}	PUNCT
ijassa-899	76	30	,	,	PUNCT
ijassa-899	76	31	db	db	PROPN
ijassa-899	76	32	=	=	SYM
ijassa-899	76	33	d−	d−	PROPN
ijassa-899	76	34	da	da	PROPN
ijassa-899	76	35	.	.	PUNCT
ijassa-899	76	36	vectors	vector	NOUN
ijassa-899	76	37	and	and	CCONJ
ijassa-899	76	38	matrices	matrix	NOUN
ijassa-899	76	39	are	be	AUX
ijassa-899	76	40	represented	represent	VERB
ijassa-899	76	41	as	as	SCONJ
ijassa-899	76	42	follows	follow	VERB
ijassa-899	76	43	x	x	X
ijassa-899	76	44	=	=	SYM
ijassa-899	76	45	(	(	PUNCT
ijassa-899	76	46	xa	xa	PROPN
ijassa-899	76	47	xb	xb	PROPN
ijassa-899	76	48	)	)	PUNCT
ijassa-899	76	49	,	,	PUNCT
ijassa-899	76	50	µ	µ	X
ijassa-899	76	51	=	=	PUNCT
ijassa-899	76	52	(	(	PUNCT
ijassa-899	76	53	µa	µa	INTJ
ijassa-899	76	54	µb	µb	VERB
ijassa-899	76	55	)	)	PUNCT
ijassa-899	76	56	,	,	PUNCT
ijassa-899	76	57	σ	σ	X
ijassa-899	77	1	=	=	PUNCT
ijassa-899	77	2	(	(	PUNCT
ijassa-899	77	3	σaa	σaa	ADV
ijassa-899	77	4	σab	σab	ADV
ijassa-899	77	5	σt	σt	ADP
ijassa-899	77	6	ab	ab	PROPN
ijassa-899	77	7	σbb	σbb	PROPN
ijassa-899	77	8	)	)	PUNCT
ijassa-899	77	9	,	,	PUNCT
ijassa-899	77	10	where	where	SCONJ
ijassa-899	77	11	xa	xa	PROPN
ijassa-899	77	12	∈	∈	PROPN
ijassa-899	77	13	rda	rda	NOUN
ijassa-899	77	14	,	,	PUNCT
ijassa-899	77	15	xb	xb	PROPN
ijassa-899	77	16	∈	∈	PROPN
ijassa-899	77	17	rdb	rdb	PROPN
ijassa-899	77	18	,	,	PUNCT
ijassa-899	77	19	µa	µa	PROPN
ijassa-899	77	20	∈	∈	NOUN
ijassa-899	77	21	rda	rda	NOUN
ijassa-899	77	22	,	,	PUNCT
ijassa-899	77	23	µb	µb	ADP
ijassa-899	77	24	∈	∈	PROPN
ijassa-899	77	25	rdb	rdb	NOUN
ijassa-899	77	26	,	,	PUNCT
ijassa-899	77	27	σaa	σaa	ADJ
ijassa-899	77	28	∈	∈	PROPN
ijassa-899	77	29	rda×da	rda×da	NOUN
ijassa-899	77	30	,	,	PUNCT
ijassa-899	77	31	σab	σab	ADJ
ijassa-899	77	32	∈	∈	PROPN
ijassa-899	77	33	rda×db	rda×db	NOUN
ijassa-899	77	34	,	,	PUNCT
ijassa-899	77	35	σbb	σbb	NOUN
ijassa-899	77	36	∈	∈	PROPN
ijassa-899	77	37	rdb×db	rdb×db	NOUN
ijassa-899	77	38	.	.	PUNCT
ijassa-899	78	1	the	the	DET
ijassa-899	78	2	following	follow	VERB
ijassa-899	78	3	property	property	NOUN
ijassa-899	78	4	is	be	AUX
ijassa-899	78	5	known	know	VERB
ijassa-899	78	6	[	[	PUNCT
ijassa-899	78	7	10	10	NUM
ijassa-899	78	8	]	]	PUNCT
ijassa-899	78	9	.	.	PUNCT
ijassa-899	79	1	claim	claim	NOUN
ijassa-899	79	2	2.2	2.2	NUM
ijassa-899	79	3	:	:	PUNCT
ijassa-899	79	4	let	let	VERB
ijassa-899	79	5	random	random	ADJ
ijassa-899	79	6	vector	vector	NOUN
ijassa-899	79	7	x	x	AUX
ijassa-899	79	8	have	have	VERB
ijassa-899	79	9	normal	normal	ADJ
ijassa-899	79	10	distributionn	distributionn	NOUN
ijassa-899	79	11	(	(	PUNCT
ijassa-899	79	12	µ,σ	µ,σ	NOUN
ijassa-899	79	13	)	)	PUNCT
ijassa-899	79	14	.	.	PUNCT
ijassa-899	80	1	then	then	ADV
ijassa-899	80	2	random	random	ADJ
ijassa-899	80	3	vector	vector	NOUN
ijassa-899	80	4	xa	xa	PROPN
ijassa-899	80	5	has	have	VERB
ijassa-899	80	6	normal	normal	ADJ
ijassa-899	80	7	distribution	distribution	NOUN
ijassa-899	80	8	n	n	CCONJ
ijassa-899	80	9	(	(	PUNCT
ijassa-899	80	10	µa	µa	NOUN
ijassa-899	80	11	,	,	PUNCT
ijassa-899	80	12	σaa	σaa	NOUN
ijassa-899	80	13	)	)	PUNCT
ijassa-899	80	14	.	.	PUNCT
ijassa-899	81	1	if	if	SCONJ
ijassa-899	81	2	random	random	ADJ
ijassa-899	81	3	vector	vector	NOUN
ijassa-899	81	4	x	x	PUNCT
ijassa-899	81	5	has	have	VERB
ijassa-899	81	6	student	student	NOUN
ijassa-899	81	7	distribution	distribution	NOUN
ijassa-899	81	8	tν(µ,σ	tν(µ,σ	NUM
ijassa-899	81	9	)	)	PUNCT
ijassa-899	81	10	,	,	PUNCT
ijassa-899	81	11	then	then	ADV
ijassa-899	81	12	random	random	ADJ
ijassa-899	81	13	vector	vector	NOUN
ijassa-899	81	14	xa	xa	PROPN
ijassa-899	81	15	has	have	VERB
ijassa-899	81	16	student	student	NOUN
ijassa-899	81	17	distribution	distribution	NOUN
ijassa-899	81	18	tν(µa	tν(µa	NOUN
ijassa-899	81	19	,	,	PUNCT
ijassa-899	81	20	σa	σa	PROPN
ijassa-899	81	21	)	)	PUNCT
ijassa-899	81	22	.	.	PUNCT
ijassa-899	82	1	2.5	2.5	NUM
ijassa-899	82	2	.	.	PUNCT
ijassa-899	83	1	conditional	conditional	ADJ
ijassa-899	83	2	distribution	distribution	NOUN
ijassa-899	83	3	our	our	PRON
ijassa-899	83	4	model	model	NOUN
ijassa-899	83	5	of	of	ADP
ijassa-899	83	6	probability	probability	NOUN
ijassa-899	83	7	distributions	distribution	NOUN
ijassa-899	83	8	mixture	mixture	NOUN
ijassa-899	83	9	allows	allow	VERB
ijassa-899	83	10	to	to	PART
ijassa-899	83	11	build	build	VERB
ijassa-899	83	12	a	a	DET
ijassa-899	83	13	regression	regression	NOUN
ijassa-899	83	14	on	on	ADP
ijassa-899	83	15	arbitrary	arbitrary	ADJ
ijassa-899	83	16	features	feature	NOUN
ijassa-899	83	17	using	use	VERB
ijassa-899	83	18	a	a	DET
ijassa-899	83	19	conditional	conditional	ADJ
ijassa-899	83	20	distribution	distribution	NOUN
ijassa-899	83	21	.	.	PUNCT
ijassa-899	84	1	in	in	ADP
ijassa-899	84	2	this	this	DET
ijassa-899	84	3	section	section	NOUN
ijassa-899	84	4	,	,	PUNCT
ijassa-899	84	5	we	we	PRON
ijassa-899	84	6	recall	recall	VERB
ijassa-899	84	7	relations	relation	NOUN
ijassa-899	84	8	for	for	ADP
ijassa-899	84	9	parameters	parameter	NOUN
ijassa-899	84	10	of	of	ADP
ijassa-899	84	11	conditional	conditional	ADJ
ijassa-899	84	12	distribution	distribution	NOUN
ijassa-899	84	13	for	for	ADP
ijassa-899	84	14	a	a	DET
ijassa-899	84	15	normal	normal	ADJ
ijassa-899	84	16	vector	vector	NOUN
ijassa-899	84	17	(	(	PUNCT
ijassa-899	84	18	claim	claim	NOUN
ijassa-899	84	19	2.3	2.3	NUM
ijassa-899	84	20	)	)	PUNCT
ijassa-899	84	21	and	and	CCONJ
ijassa-899	84	22	for	for	ADP
ijassa-899	84	23	a	a	DET
ijassa-899	84	24	student	student	NOUN
ijassa-899	84	25	vector	vector	NOUN
ijassa-899	84	26	(	(	PUNCT
ijassa-899	84	27	theorem	theorem	VERB
ijassa-899	84	28	2.1	2.1	NUM
ijassa-899	84	29	)	)	PUNCT
ijassa-899	84	30	.	.	PUNCT
ijassa-899	85	1	let	let	VERB
ijassa-899	85	2	random	random	ADJ
ijassa-899	85	3	vectorx	vectorx	NOUN
ijassa-899	85	4	have	have	VERB
ijassa-899	85	5	normal	normal	ADJ
ijassa-899	85	6	distributionn	distributionn	NOUN
ijassa-899	85	7	(	(	PUNCT
ijassa-899	85	8	µ,σ	µ,σ	NOUN
ijassa-899	85	9	)	)	PUNCT
ijassa-899	85	10	,	,	PUNCT
ijassa-899	85	11	where	where	SCONJ
ijassa-899	85	12	σ	σ	PROPN
ijassa-899	85	13	is	be	AUX
ijassa-899	85	14	positive	positive	ADJ
ijassa-899	85	15	semi	semi	ADJ
ijassa-899	85	16	-	-	ADJ
ijassa-899	85	17	definite	definite	ADJ
ijassa-899	85	18	scale	scale	NOUN
ijassa-899	85	19	matrix	matrix	NOUN
ijassa-899	85	20	.	.	PUNCT
ijassa-899	86	1	moreover	moreover	ADV
ijassa-899	86	2	let	let	VERB
ijassa-899	86	3	a	a	DET
ijassa-899	86	4	,	,	PUNCT
ijassa-899	86	5	b	b	NOUN
ijassa-899	86	6	,	,	PUNCT
ijassa-899	86	7	c	c	PROPN
ijassa-899	86	8	are	be	AUX
ijassa-899	86	9	disjoint	disjoint	NOUN
ijassa-899	86	10	sets	set	NOUN
ijassa-899	86	11	of	of	ADP
ijassa-899	86	12	indexes	index	NOUN
ijassa-899	86	13	,	,	PUNCT
ijassa-899	86	14	and	and	CCONJ
ijassa-899	86	15	a	a	DET
ijassa-899	86	16	t	t	NOUN
ijassa-899	86	17	b	b	PROPN
ijassa-899	86	18	t	t	NOUN
ijassa-899	86	19	c	c	NOUN
ijassa-899	86	20	=	=	SYM
ijassa-899	86	21	{	{	PUNCT
ijassa-899	86	22	1	1	NUM
ijassa-899	86	23	,	,	PUNCT
ijassa-899	86	24	...	...	PUNCT
ijassa-899	86	25	,	,	PUNCT
ijassa-899	86	26	d	d	NOUN
ijassa-899	86	27	}	}	PUNCT
ijassa-899	86	28	.	.	PUNCT
ijassa-899	87	1	without	without	ADP
ijassa-899	87	2	loss	loss	NOUN
ijassa-899	87	3	of	of	ADP
ijassa-899	87	4	generality	generality	NOUN
ijassa-899	87	5	,	,	PUNCT
ijassa-899	87	6	set	set	VERB
ijassa-899	87	7	a	a	PRON
ijassa-899	87	8	=	=	PUNCT
ijassa-899	87	9	{	{	PUNCT
ijassa-899	87	10	1	1	NUM
ijassa-899	87	11	,	,	PUNCT
ijassa-899	87	12	...	...	PUNCT
ijassa-899	87	13	,	,	PUNCT
ijassa-899	87	14	da	da	ADJ
ijassa-899	87	15	}	}	PUNCT
ijassa-899	87	16	,	,	PUNCT
ijassa-899	87	17	b	b	X
ijassa-899	87	18	=	=	PRON
ijassa-899	87	19	{	{	PUNCT
ijassa-899	87	20	da	da	NOUN
ijassa-899	87	21	+	+	NUM
ijassa-899	87	22	1	1	NUM
ijassa-899	87	23	,	,	PUNCT
ijassa-899	87	24	...	...	PUNCT
ijassa-899	87	25	,	,	PUNCT
ijassa-899	87	26	da	da	PROPN
ijassa-899	87	27	+	+	CCONJ
ijassa-899	87	28	db	db	PROPN
ijassa-899	87	29	}	}	PUNCT
ijassa-899	87	30	,	,	PUNCT
ijassa-899	87	31	c	c	X
ijassa-899	87	32	=	=	PRON
ijassa-899	87	33	{	{	PUNCT
ijassa-899	87	34	da	da	PROPN
ijassa-899	87	35	+	+	X
ijassa-899	87	36	db	db	PROPN
ijassa-899	87	37	+	+	ADJ
ijassa-899	87	38	1	1	NUM
ijassa-899	87	39	,	,	PUNCT
ijassa-899	87	40	...	...	PUNCT
ijassa-899	87	41	,	,	PUNCT
ijassa-899	87	42	d	d	NOUN
ijassa-899	87	43	}	}	PUNCT
ijassa-899	87	44	.	.	PUNCT
ijassa-899	88	1	vectors	vector	NOUN
ijassa-899	88	2	and	and	CCONJ
ijassa-899	88	3	matrices	matrix	NOUN
ijassa-899	88	4	are	be	AUX
ijassa-899	88	5	represented	represent	VERB
ijassa-899	88	6	as	as	SCONJ
ijassa-899	88	7	follows	follow	VERB
ijassa-899	88	8	x	x	X
ijassa-899	88	9	=	=	SYM
ijassa-899	88	10	(	(	PUNCT
ijassa-899	88	11	xa	xa	PROPN
ijassa-899	88	12	xb	xb	PROPN
ijassa-899	88	13	xc	xc	PROPN
ijassa-899	88	14	)	)	PUNCT
ijassa-899	88	15	,	,	PUNCT
ijassa-899	88	16	µ	µ	X
ijassa-899	88	17	=	=	PUNCT
ijassa-899	88	18	(	(	PUNCT
ijassa-899	88	19	µa	µa	ADV
ijassa-899	88	20	µb	µb	VERB
ijassa-899	88	21	µc	µc	ADP
ijassa-899	88	22	)	)	PUNCT
ijassa-899	88	23	,	,	PUNCT
ijassa-899	88	24	σ	σ	X
ijassa-899	89	1	=	=	PRON
ijassa-899	89	2	(	(	PUNCT
ijassa-899	89	3	σaa	σaa	ADV
ijassa-899	89	4	σab	σab	ADV
ijassa-899	89	5	σac	σac	VERB
ijassa-899	89	6	σt	σt	ADP
ijassa-899	89	7	ab	ab	PROPN
ijassa-899	89	8	σbb	σbb	ADJ
ijassa-899	89	9	σbc	σbc	ADV
ijassa-899	89	10	σt	σt	PART
ijassa-899	89	11	ac	ac	PROPN
ijassa-899	89	12	σt	σt	ADP
ijassa-899	89	13	bc	bc	PROPN
ijassa-899	89	14	σcc	σcc	PROPN
ijassa-899	89	15	)	)	PUNCT
ijassa-899	89	16	.	.	PUNCT
ijassa-899	90	1	copyright	copyright	NOUN
ijassa-899	90	2	©	©	PROPN
ijassa-899	90	3	2020	2020	NUM
ijassa-899	90	4	assa	assa	NOUN
ijassa-899	90	5	.	.	PUNCT
ijassa-899	91	1	adv	adv	PROPN
ijassa-899	91	2	syst	syst	PROPN
ijassa-899	91	3	sci	sci	PROPN
ijassa-899	91	4	appl	appl	PROPN
ijassa-899	91	5	(	(	PUNCT
ijassa-899	91	6	2020	2020	NUM
ijassa-899	91	7	)	)	PUNCT
ijassa-899	91	8	student	student	NOUN
ijassa-899	91	9	mixture	mixture	NOUN
ijassa-899	91	10	and	and	CCONJ
ijassa-899	91	11	its	its	PRON
ijassa-899	91	12	machine	machine	NOUN
ijassa-899	91	13	learning	learn	VERB
ijassa-899	91	14	applications	application	NOUN
ijassa-899	91	15	to	to	ADP
ijassa-899	91	16	pvt	pvt	PROPN
ijassa-899	91	17	properties	property	NOUN
ijassa-899	91	18	101	101	NUM
ijassa-899	91	19	denote	denote	NOUN
ijassa-899	91	20	also	also	ADV
ijassa-899	91	21	λ	λ	X
ijassa-899	91	22	=	=	SYM
ijassa-899	91	23	(	(	PUNCT
ijassa-899	91	24	λaa	λaa	PROPN
ijassa-899	91	25	λab	λab	NOUN
ijassa-899	91	26	λt	λt	ADP
ijassa-899	91	27	ab	ab	PROPN
ijassa-899	91	28	λbb	λbb	PROPN
ijassa-899	91	29	)	)	PUNCT
ijassa-899	92	1	=	=	PRON
ijassa-899	92	2	(	(	PUNCT
ijassa-899	92	3	σaa	σaa	ADV
ijassa-899	92	4	σab	σab	ADV
ijassa-899	92	5	σt	σt	ADP
ijassa-899	92	6	ab	ab	PROPN
ijassa-899	92	7	σbb	σbb	PROPN
ijassa-899	92	8	)	)	PUNCT
ijassa-899	92	9	−1	−1	NOUN
ijassa-899	92	10	.	.	PUNCT
ijassa-899	93	1	claim	claim	VERB
ijassa-899	93	2	2.3	2.3	NUM
ijassa-899	93	3	:	:	PUNCT
ijassa-899	93	4	vector	vector	NOUN
ijassa-899	93	5	xa	xa	PROPN
ijassa-899	93	6	conditioned	condition	VERB
ijassa-899	93	7	on	on	ADP
ijassa-899	93	8	xb	xb	PROPN
ijassa-899	93	9	has	have	VERB
ijassa-899	93	10	distribution	distribution	NOUN
ijassa-899	93	11	n	n	CCONJ
ijassa-899	93	12	(	(	PUNCT
ijassa-899	93	13	µ̃	µ̃	PROPN
ijassa-899	93	14	,	,	PUNCT
ijassa-899	93	15	σ̃	σ̃	PROPN
ijassa-899	93	16	)	)	PUNCT
ijassa-899	93	17	,	,	PUNCT
ijassa-899	93	18	where	where	SCONJ
ijassa-899	93	19	µ̃	µ̃	PROPN
ijassa-899	93	20	=	=	PUNCT
ijassa-899	93	21	µa	µa	ADP
ijassa-899	93	22	−	−	PROPN
ijassa-899	94	1	λ−1	λ−1	NOUN
ijassa-899	94	2	aa	aa	NOUN
ijassa-899	94	3	λab(xb	λab(xb	VERB
ijassa-899	94	4	−	−	PROPN
ijassa-899	94	5	µb	µb	NOUN
ijassa-899	94	6	)	)	PUNCT
ijassa-899	94	7	,	,	PUNCT
ijassa-899	94	8	σ̃	σ̃	PROPN
ijassa-899	94	9	=	=	PUNCT
ijassa-899	95	1	λ−1	λ−1	PROPN
ijassa-899	95	2	aa	aa	NOUN
ijassa-899	95	3	.	.	PUNCT
ijassa-899	96	1	the	the	DET
ijassa-899	96	2	proof	proof	NOUN
ijassa-899	96	3	of	of	ADP
ijassa-899	96	4	this	this	DET
ijassa-899	96	5	statement	statement	NOUN
ijassa-899	96	6	is	be	AUX
ijassa-899	96	7	given	give	VERB
ijassa-899	96	8	in	in	ADP
ijassa-899	96	9	[	[	X
ijassa-899	96	10	10	10	NUM
ijassa-899	96	11	]	]	PUNCT
ijassa-899	96	12	.	.	PUNCT
ijassa-899	97	1	it	it	PRON
ijassa-899	97	2	follows	follow	VERB
ijassa-899	97	3	that	that	SCONJ
ijassa-899	97	4	the	the	DET
ijassa-899	97	5	conditional	conditional	ADJ
ijassa-899	97	6	distributions	distribution	NOUN
ijassa-899	97	7	of	of	ADP
ijassa-899	97	8	the	the	DET
ijassa-899	97	9	components	component	NOUN
ijassa-899	97	10	of	of	ADP
ijassa-899	97	11	a	a	DET
ijassa-899	97	12	normal	normal	ADJ
ijassa-899	97	13	vector	vector	NOUN
ijassa-899	97	14	are	be	AUX
ijassa-899	97	15	also	also	ADV
ijassa-899	97	16	normal	normal	ADJ
ijassa-899	97	17	.	.	PUNCT
ijassa-899	98	1	let	let	VERB
ijassa-899	98	2	us	we	PRON
ijassa-899	98	3	now	now	ADV
ijassa-899	98	4	consider	consider	VERB
ijassa-899	98	5	the	the	DET
ijassa-899	98	6	case	case	NOUN
ijassa-899	98	7	in	in	ADP
ijassa-899	98	8	which	which	PRON
ijassa-899	98	9	the	the	DET
ijassa-899	98	10	vector	vector	NOUN
ijassa-899	98	11	x	x	PUNCT
ijassa-899	98	12	has	have	VERB
ijassa-899	98	13	a	a	DET
ijassa-899	98	14	student	student	NOUN
ijassa-899	98	15	distribution	distribution	NOUN
ijassa-899	98	16	tν(µ,σ	tν(µ,σ	NUM
ijassa-899	98	17	)	)	PUNCT
ijassa-899	99	1	[	[	X
ijassa-899	99	2	5	5	NUM
ijassa-899	99	3	]	]	PUNCT
ijassa-899	99	4	.	.	PUNCT
ijassa-899	100	1	theorem	theorem	VERB
ijassa-899	100	2	2.1	2.1	NUM
ijassa-899	100	3	:	:	PUNCT
ijassa-899	100	4	vector	vector	NOUN
ijassa-899	100	5	xa	xa	PROPN
ijassa-899	100	6	conditioned	condition	VERB
ijassa-899	100	7	on	on	ADP
ijassa-899	100	8	xb	xb	PROPN
ijassa-899	100	9	has	have	VERB
ijassa-899	100	10	distribution	distribution	NOUN
ijassa-899	100	11	tν̃(µ̃	tν̃(µ̃	NOUN
ijassa-899	100	12	,	,	PUNCT
ijassa-899	100	13	σ̃	σ̃	PROPN
ijassa-899	100	14	)	)	PUNCT
ijassa-899	100	15	,	,	PUNCT
ijassa-899	100	16	for	for	ADP
ijassa-899	100	17	where	where	SCONJ
ijassa-899	101	1	ν̃	ν̃	PROPN
ijassa-899	101	2	=	=	SYM
ijassa-899	101	3	ν	ν	X
ijassa-899	101	4	+	+	CCONJ
ijassa-899	101	5	db	db	PROPN
ijassa-899	101	6	,	,	PUNCT
ijassa-899	101	7	µ̃	µ̃	PROPN
ijassa-899	101	8	=	=	PUNCT
ijassa-899	101	9	µa	µa	ADP
ijassa-899	101	10	−	−	PROPN
ijassa-899	102	1	λ−1	λ−1	NOUN
ijassa-899	102	2	aa	aa	NOUN
ijassa-899	102	3	λab(xb	λab(xb	VERB
ijassa-899	102	4	−	−	PROPN
ijassa-899	102	5	µb	µb	NOUN
ijassa-899	102	6	)	)	PUNCT
ijassa-899	102	7	,	,	PUNCT
ijassa-899	102	8	σ̃	σ̃	PROPN
ijassa-899	102	9	=	=	PUNCT
ijassa-899	103	1	ν	ν	X
ijassa-899	103	2	+	+	NUM
ijassa-899	103	3	ϕ(xb	ϕ(xb	NOUN
ijassa-899	103	4	)	)	PUNCT
ijassa-899	103	5	ν	ν	NOUN
ijassa-899	103	6	+	+	CCONJ
ijassa-899	103	7	db	db	VERB
ijassa-899	103	8	λ−1	λ−1	PROPN
ijassa-899	103	9	aa	aa	NOUN
ijassa-899	103	10	,	,	PUNCT
ijassa-899	103	11	ϕ(x	ϕ(x	X
ijassa-899	103	12	)	)	PUNCT
ijassa-899	103	13	=	=	SYM
ijassa-899	104	1	(	(	PUNCT
ijassa-899	104	2	x−	x−	PROPN
ijassa-899	104	3	µb)t	µb)t	PROPN
ijassa-899	104	4	(	(	PUNCT
ijassa-899	104	5	λbb	λbb	NOUN
ijassa-899	104	6	−	−	NOUN
ijassa-899	104	7	λt	λt	ADP
ijassa-899	104	8	abλ	abλ	ADV
ijassa-899	104	9	−1	−1	ADV
ijassa-899	104	10	aa	aa	NOUN
ijassa-899	104	11	λab	λab	PROPN
ijassa-899	104	12	)	)	PUNCT
ijassa-899	104	13	(	(	PUNCT
ijassa-899	104	14	x−	x−	PROPN
ijassa-899	104	15	µb	µb	PROPN
ijassa-899	104	16	)	)	PUNCT
ijassa-899	104	17	.	.	PUNCT
ijassa-899	105	1	remark	remark	PROPN
ijassa-899	105	2	.	.	PUNCT
ijassa-899	106	1	function	function	PROPN
ijassa-899	106	2	ϕ(x	ϕ(x	PROPN
ijassa-899	106	3	)	)	PUNCT
ijassa-899	106	4	is	be	AUX
ijassa-899	106	5	non	non	ADJ
ijassa-899	106	6	-	-	ADJ
ijassa-899	106	7	negative	negative	ADJ
ijassa-899	106	8	since	since	SCONJ
ijassa-899	106	9	all	all	DET
ijassa-899	106	10	angular	angular	ADJ
ijassa-899	106	11	minors	minor	NOUN
ijassa-899	106	12	of	of	ADP
ijassa-899	106	13	the	the	DET
ijassa-899	106	14	matrix	matrix	NOUN
ijassa-899	106	15	λbb	λbb	VERB
ijassa-899	106	16	−	−	NOUN
ijassa-899	106	17	λt	λt	ADP
ijassa-899	106	18	abλ	abλ	ADV
ijassa-899	106	19	−1	−1	ADV
ijassa-899	106	20	aa	aa	NOUN
ijassa-899	106	21	λab	λab	NOUN
ijassa-899	106	22	are	be	AUX
ijassa-899	106	23	positive	positive	ADJ
ijassa-899	106	24	.	.	PUNCT
ijassa-899	107	1	the	the	DET
ijassa-899	107	2	latter	latter	ADJ
ijassa-899	107	3	fact	fact	NOUN
ijassa-899	107	4	follows	follow	VERB
ijassa-899	107	5	from	from	ADP
ijassa-899	107	6	the	the	DET
ijassa-899	107	7	block	block	NOUN
ijassa-899	107	8	matrix	matrix	NOUN
ijassa-899	107	9	determinant	determinant	ADJ
ijassa-899	107	10	formula	formula	NOUN
ijassa-899	107	11	[	[	X
ijassa-899	107	12	11	11	NUM
ijassa-899	107	13	]	]	PUNCT
ijassa-899	107	14	and	and	CCONJ
ijassa-899	107	15	positive	positive	ADJ
ijassa-899	107	16	definiteness	definiteness	NOUN
ijassa-899	107	17	of	of	ADP
ijassa-899	107	18	the	the	DET
ijassa-899	107	19	scale	scale	NOUN
ijassa-899	107	20	matrix	matrix	NOUN
ijassa-899	107	21	.	.	PUNCT
ijassa-899	108	1	3	3	X
ijassa-899	108	2	.	.	X
ijassa-899	108	3	mixture	mixture	NOUN
ijassa-899	108	4	model	model	NOUN
ijassa-899	108	5	in	in	ADP
ijassa-899	108	6	this	this	DET
ijassa-899	108	7	section	section	NOUN
ijassa-899	108	8	,	,	PUNCT
ijassa-899	108	9	we	we	PRON
ijassa-899	108	10	formalize	formalize	VERB
ijassa-899	108	11	the	the	DET
ijassa-899	108	12	concept	concept	NOUN
ijassa-899	108	13	of	of	ADP
ijassa-899	108	14	a	a	DET
ijassa-899	108	15	mixture	mixture	NOUN
ijassa-899	108	16	of	of	ADP
ijassa-899	108	17	probability	probability	NOUN
ijassa-899	108	18	distributions	distribution	NOUN
ijassa-899	108	19	and	and	CCONJ
ijassa-899	108	20	give	give	VERB
ijassa-899	108	21	its	its	PRON
ijassa-899	108	22	properties	property	NOUN
ijassa-899	108	23	that	that	PRON
ijassa-899	108	24	appear	appear	VERB
ijassa-899	108	25	helpful	helpful	ADJ
ijassa-899	108	26	for	for	ADP
ijassa-899	108	27	solving	solve	VERB
ijassa-899	108	28	machine	machine	NOUN
ijassa-899	108	29	learning	learning	NOUN
ijassa-899	108	30	problems	problem	NOUN
ijassa-899	108	31	mentioned	mention	VERB
ijassa-899	108	32	in	in	ADP
ijassa-899	108	33	introduction	introduction	NOUN
ijassa-899	108	34	.	.	PUNCT
ijassa-899	109	1	in	in	ADP
ijassa-899	109	2	addition	addition	NOUN
ijassa-899	109	3	,	,	PUNCT
ijassa-899	109	4	iterative	iterative	NOUN
ijassa-899	109	5	methods	method	NOUN
ijassa-899	109	6	for	for	ADP
ijassa-899	109	7	estimating	estimate	VERB
ijassa-899	109	8	parameters	parameter	NOUN
ijassa-899	109	9	of	of	ADP
ijassa-899	109	10	a	a	DET
ijassa-899	109	11	mixture	mixture	NOUN
ijassa-899	109	12	of	of	ADP
ijassa-899	109	13	normal	normal	ADJ
ijassa-899	109	14	distributions	distribution	NOUN
ijassa-899	109	15	and	and	CCONJ
ijassa-899	109	16	a	a	DET
ijassa-899	109	17	mixture	mixture	NOUN
ijassa-899	109	18	of	of	ADP
ijassa-899	109	19	student	student	NOUN
ijassa-899	109	20	distributions	distribution	NOUN
ijassa-899	109	21	are	be	AUX
ijassa-899	109	22	provided	provide	VERB
ijassa-899	109	23	.	.	PUNCT
ijassa-899	110	1	3.1	3.1	NUM
ijassa-899	110	2	.	.	PUNCT
ijassa-899	110	3	properties	property	NOUN
ijassa-899	110	4	of	of	ADP
ijassa-899	110	5	a	a	DET
ijassa-899	110	6	mixture	mixture	NOUN
ijassa-899	110	7	model	model	NOUN
ijassa-899	110	8	consider	consider	VERB
ijassa-899	110	9	a	a	DET
ijassa-899	110	10	mixture	mixture	NOUN
ijassa-899	110	11	of	of	ADP
ijassa-899	110	12	probability	probability	NOUN
ijassa-899	110	13	distributions	distribution	NOUN
ijassa-899	110	14	p	p	X
ijassa-899	110	15	=	=	SYM
ijassa-899	110	16	k∑	k∑	PROPN
ijassa-899	110	17	j=1	j=1	PROPN
ijassa-899	110	18	wjpj	wjpj	PROPN
ijassa-899	110	19	,	,	PUNCT
ijassa-899	110	20	where	where	SCONJ
ijassa-899	110	21	pj	pj	PROPN
ijassa-899	110	22	is	be	AUX
ijassa-899	110	23	a	a	DET
ijassa-899	110	24	”	"	PUNCT
ijassa-899	110	25	simple	simple	ADJ
ijassa-899	110	26	”	"	PUNCT
ijassa-899	110	27	probability	probability	NOUN
ijassa-899	110	28	distribution	distribution	NOUN
ijassa-899	110	29	that	that	PRON
ijassa-899	110	30	defines	define	VERB
ijassa-899	110	31	the	the	DET
ijassa-899	110	32	component	component	NOUN
ijassa-899	110	33	of	of	ADP
ijassa-899	110	34	the	the	DET
ijassa-899	110	35	mixture	mixture	NOUN
ijassa-899	110	36	and	and	CCONJ
ijassa-899	110	37	wj	wj	NOUN
ijassa-899	110	38	∈	∈	PROPN
ijassa-899	111	1	[	[	X
ijassa-899	111	2	0	0	NUM
ijassa-899	111	3	,	,	PUNCT
ijassa-899	111	4	1	1	NUM
ijassa-899	111	5	]	]	PUNCT
ijassa-899	111	6	are	be	AUX
ijassa-899	111	7	components	component	NOUN
ijassa-899	111	8	weights	weight	NOUN
ijassa-899	111	9	,	,	PUNCT
ijassa-899	111	10	∑n	∑n	PROPN
ijassa-899	111	11	i=1	i=1	PROPN
ijassa-899	111	12	wj	wj	NOUN
ijassa-899	111	13	=	=	NOUN
ijassa-899	111	14	1	1	X
ijassa-899	111	15	.	.	PUNCT
ijassa-899	112	1	these	these	DET
ijassa-899	112	2	components	component	NOUN
ijassa-899	112	3	are	be	AUX
ijassa-899	112	4	often	often	ADV
ijassa-899	112	5	called	call	VERB
ijassa-899	112	6	clusters	cluster	NOUN
ijassa-899	112	7	.	.	PUNCT
ijassa-899	113	1	a	a	DET
ijassa-899	113	2	random	random	ADJ
ijassa-899	113	3	vector	vector	NOUN
ijassa-899	113	4	x	x	PRON
ijassa-899	113	5	obeys	obey	VERB
ijassa-899	113	6	the	the	DET
ijassa-899	113	7	model	model	NOUN
ijassa-899	113	8	of	of	ADP
ijassa-899	113	9	a	a	DET
ijassa-899	113	10	mixture	mixture	NOUN
ijassa-899	113	11	of	of	ADP
ijassa-899	113	12	distributions	distribution	NOUN
ijassa-899	113	13	if	if	SCONJ
ijassa-899	113	14	it	it	PRON
ijassa-899	113	15	is	be	AUX
ijassa-899	113	16	represented	represent	VERB
ijassa-899	113	17	as	as	ADP
ijassa-899	113	18	x	x	X
ijassa-899	113	19	=	=	PUNCT
ijassa-899	113	20	k∑	k∑	PROPN
ijassa-899	114	1	j=1	j=1	NOUN
ijassa-899	114	2	xji{t	xji{t	PUNCT
ijassa-899	115	1	=	=	SYM
ijassa-899	115	2	j	j	NOUN
ijassa-899	115	3	}	}	PUNCT
ijassa-899	115	4	,	,	PUNCT
ijassa-899	115	5	where	where	SCONJ
ijassa-899	115	6	xj	xj	PROPN
ijassa-899	115	7	is	be	AUX
ijassa-899	115	8	distributed	distribute	VERB
ijassa-899	115	9	as	as	ADP
ijassa-899	115	10	pj	pj	PROPN
ijassa-899	115	11	and	and	CCONJ
ijassa-899	115	12	random	random	ADJ
ijassa-899	115	13	variable	variable	NOUN
ijassa-899	115	14	t	t	PROPN
ijassa-899	115	15	is	be	AUX
ijassa-899	115	16	equal	equal	ADJ
ijassa-899	115	17	to	to	ADP
ijassa-899	115	18	the	the	DET
ijassa-899	115	19	cluster	cluster	NOUN
ijassa-899	115	20	number	number	NOUN
ijassa-899	115	21	,	,	PUNCT
ijassa-899	115	22	i.e.	i.e.	X
ijassa-899	115	23	p(t	p(t	NOUN
ijassa-899	115	24	=	=	SYM
ijassa-899	115	25	j	j	NOUN
ijassa-899	115	26	)	)	PUNCT
ijassa-899	115	27	=	=	SYM
ijassa-899	115	28	wj	wj	PROPN
ijassa-899	115	29	.	.	PUNCT
ijassa-899	116	1	moreover	moreover	ADV
ijassa-899	116	2	,	,	PUNCT
ijassa-899	116	3	variables	variable	NOUN
ijassa-899	116	4	xj	xj	PROPN
ijassa-899	116	5	are	be	AUX
ijassa-899	116	6	independent	independent	ADJ
ijassa-899	116	7	of	of	ADP
ijassa-899	116	8	t	t	PROPN
ijassa-899	116	9	.	.	PUNCT
ijassa-899	117	1	next	next	ADJ
ijassa-899	117	2	statement	statement	NOUN
ijassa-899	117	3	can	can	AUX
ijassa-899	117	4	be	be	AUX
ijassa-899	117	5	found	find	VERB
ijassa-899	117	6	in	in	ADP
ijassa-899	117	7	[	[	X
ijassa-899	117	8	12	12	NUM
ijassa-899	117	9	]	]	PUNCT
ijassa-899	117	10	.	.	PUNCT
ijassa-899	118	1	claim	claim	NOUN
ijassa-899	118	2	3.1	3.1	NUM
ijassa-899	118	3	:	:	PUNCT
ijassa-899	118	4	let	let	VERB
ijassa-899	118	5	exj	exj	NOUN
ijassa-899	118	6	=	=	SYM
ijassa-899	118	7	µj	µj	PROPN
ijassa-899	118	8	,	,	PUNCT
ijassa-899	118	9	varxj	varxj	NOUN
ijassa-899	118	10	=	=	PRON
ijassa-899	118	11	σj	σj	PROPN
ijassa-899	118	12	(	(	PUNCT
ijassa-899	118	13	their	their	PRON
ijassa-899	118	14	existence	existence	NOUN
ijassa-899	118	15	is	be	AUX
ijassa-899	118	16	assumed	assume	VERB
ijassa-899	118	17	)	)	PUNCT
ijassa-899	118	18	.	.	PUNCT
ijassa-899	119	1	then	then	ADV
ijassa-899	119	2	the	the	DET
ijassa-899	119	3	expectation	expectation	NOUN
ijassa-899	119	4	and	and	CCONJ
ijassa-899	119	5	copyright	copyright	NOUN
ijassa-899	119	6	©	©	PROPN
ijassa-899	119	7	2020	2020	NUM
ijassa-899	119	8	assa	assa	NOUN
ijassa-899	119	9	.	.	PUNCT
ijassa-899	120	1	adv	adv	PROPN
ijassa-899	120	2	syst	syst	PROPN
ijassa-899	120	3	sci	sci	PROPN
ijassa-899	120	4	appl	appl	PROPN
ijassa-899	120	5	(	(	PUNCT
ijassa-899	120	6	2020	2020	NUM
ijassa-899	120	7	)	)	PUNCT
ijassa-899	120	8	102	102	NUM
ijassa-899	120	9	n.a	n.a	PROPN
ijassa-899	120	10	.	.	PROPN
ijassa-899	120	11	volkov	volkov	PROPN
ijassa-899	120	12	,	,	PUNCT
ijassa-899	120	13	e.yu	e.yu	PROPN
ijassa-899	120	14	.	.	PROPN
ijassa-899	120	15	dakhova	dakhova	PROPN
ijassa-899	120	16	,	,	PUNCT
ijassa-899	120	17	s.a	s.a	PROPN
ijassa-899	120	18	.	.	PROPN
ijassa-899	120	19	budennyy	budennyy	PROPN
ijassa-899	120	20	,	,	PUNCT
ijassa-899	120	21	a.m.	a.m.	PROPN
ijassa-899	120	22	andrianova	andrianova	PROPN
ijassa-899	120	23	covariance	covariance	NOUN
ijassa-899	120	24	matrix	matrix	NOUN
ijassa-899	120	25	for	for	ADP
ijassa-899	120	26	a	a	DET
ijassa-899	120	27	mixture	mixture	NOUN
ijassa-899	120	28	of	of	ADP
ijassa-899	120	29	distributions	distribution	NOUN
ijassa-899	120	30	are	be	AUX
ijassa-899	120	31	equal	equal	ADJ
ijassa-899	120	32	to	to	ADP
ijassa-899	120	33	ex	ex	X
ijassa-899	120	34	=	=	SYM
ijassa-899	120	35	k∑	k∑	ADJ
ijassa-899	120	36	j=1	j=1	PROPN
ijassa-899	120	37	wjµj	wjµj	NOUN
ijassa-899	120	38	,	,	PUNCT
ijassa-899	120	39	varx	varx	NOUN
ijassa-899	120	40	=	=	SYM
ijassa-899	120	41	k∑	k∑	PROPN
ijassa-899	121	1	j=1	j=1	PROPN
ijassa-899	121	2	wjσj	wjσj	PROPN
ijassa-899	121	3	+	+	CCONJ
ijassa-899	121	4	k∑	k∑	ADJ
ijassa-899	121	5	j=1	j=1	PROPN
ijassa-899	121	6	wjµ	wjµ	PROPN
ijassa-899	121	7	j(µj)t	j(µj)t	PROPN
ijassa-899	121	8	−	−	PROPN
ijassa-899	121	9	k∑	k∑	PROPN
ijassa-899	121	10	j	j	PROPN
ijassa-899	121	11	,	,	PUNCT
ijassa-899	121	12	s=1	s=1	PROPN
ijassa-899	121	13	wjwsµ	wjwsµ	PROPN
ijassa-899	121	14	j(µs)t	j(µs)t	PROPN
ijassa-899	121	15	.	.	PUNCT
ijassa-899	122	1	claim	claim	VERB
ijassa-899	122	2	3.2	3.2	NUM
ijassa-899	122	3	:	:	PUNCT
ijassa-899	122	4	let	let	VERB
ijassa-899	122	5	xt	xt	PUNCT
ijassa-899	122	6	=	=	PUNCT
ijassa-899	122	7	.	.	PUNCT
ijassa-899	122	8	.	.	PUNCT
ijassa-899	122	9	.	.	PUNCT
ijassa-899	123	1	(	(	PUNCT
ijassa-899	123	2	xt	xt	ADP
ijassa-899	123	3	a	a	PRON
ijassa-899	123	4	,	,	PUNCT
ijassa-899	123	5	x	x	PROPN
ijassa-899	123	6	t	t	PROPN
ijassa-899	123	7	b	b	PROPN
ijassa-899	123	8	,	,	PUNCT
ijassa-899	123	9	x	x	PROPN
ijassa-899	123	10	t	t	PROPN
ijassa-899	123	11	c	c	PROPN
ijassa-899	123	12	)	)	PUNCT
ijassa-899	123	13	.	.	PUNCT
ijassa-899	124	1	then	then	ADV
ijassa-899	124	2	conditional	conditional	ADJ
ijassa-899	124	3	probability	probability	NOUN
ijassa-899	124	4	of	of	ADP
ijassa-899	124	5	the	the	DET
ijassa-899	124	6	cluster	cluster	NOUN
ijassa-899	124	7	j	j	PROPN
ijassa-899	124	8	conditioned	condition	VERB
ijassa-899	124	9	on	on	ADP
ijassa-899	124	10	xb	xb	PROPN
ijassa-899	124	11	equals	equal	VERB
ijassa-899	124	12	w̃j	w̃j	NOUN
ijassa-899	125	1	=	=	PUNCT
ijassa-899	125	2	p(t	p(t	NOUN
ijassa-899	125	3	=	=	PUNCT
ijassa-899	125	4	j	j	PROPN
ijassa-899	125	5	|	|	NOUN
ijassa-899	125	6	xb	xb	PROPN
ijassa-899	125	7	)	)	PUNCT
ijassa-899	126	1	=	=	PRON
ijassa-899	126	2	wjp	wjp	VERB
ijassa-899	126	3	(	(	PUNCT
ijassa-899	126	4	b	b	NOUN
ijassa-899	126	5	)	)	PUNCT
ijassa-899	126	6	j	j	NOUN
ijassa-899	126	7	(	(	PUNCT
ijassa-899	126	8	xb)∑k	xb)∑k	PROPN
ijassa-899	126	9	j=1	j=1	PROPN
ijassa-899	126	10	wjp	wjp	VERB
ijassa-899	126	11	(	(	PUNCT
ijassa-899	126	12	b	b	NOUN
ijassa-899	126	13	)	)	PUNCT
ijassa-899	126	14	j	j	PROPN
ijassa-899	126	15	(	(	PUNCT
ijassa-899	126	16	xb	xb	PROPN
ijassa-899	126	17	)	)	PUNCT
ijassa-899	126	18	,	,	PUNCT
ijassa-899	126	19	where	where	SCONJ
ijassa-899	126	20	p(b	p(b	NOUN
ijassa-899	126	21	)	)	PUNCT
ijassa-899	126	22	j	j	PROPN
ijassa-899	126	23	is	be	AUX
ijassa-899	126	24	the	the	DET
ijassa-899	126	25	density	density	NOUN
ijassa-899	126	26	of	of	ADP
ijassa-899	126	27	the	the	DET
ijassa-899	126	28	vector	vector	NOUN
ijassa-899	126	29	component	component	NOUN
ijassa-899	126	30	xb	xb	PROPN
ijassa-899	126	31	conditioned	condition	VERB
ijassa-899	126	32	on	on	ADP
ijassa-899	126	33	pj	pj	PROPN
ijassa-899	126	34	.	.	PUNCT
ijassa-899	127	1	let	let	VERB
ijassa-899	127	2	vector	vector	NOUN
ijassa-899	127	3	xa	xa	PROPN
ijassa-899	127	4	has	have	VERB
ijassa-899	127	5	distribution	distribution	NOUN
ijassa-899	127	6	p̃	p̃	PROPN
ijassa-899	127	7	(	(	PUNCT
ijassa-899	127	8	a|b	a|b	NOUN
ijassa-899	127	9	)	)	PUNCT
ijassa-899	127	10	j	j	PROPN
ijassa-899	127	11	conditioned	condition	VERB
ijassa-899	127	12	on	on	ADP
ijassa-899	127	13	xb	xb	PROPN
ijassa-899	127	14	and	and	CCONJ
ijassa-899	127	15	i{t	i{t	ADV
ijassa-899	127	16	=	=	SYM
ijassa-899	127	17	j	j	PROPN
ijassa-899	127	18	}	}	PUNCT
ijassa-899	127	19	.	.	PUNCT
ijassa-899	128	1	then	then	ADV
ijassa-899	128	2	vector	vector	PROPN
ijassa-899	128	3	xa	xa	PROPN
ijassa-899	128	4	conditioned	condition	VERB
ijassa-899	128	5	on	on	ADP
ijassa-899	128	6	xb	xb	PROPN
ijassa-899	128	7	has	have	VERB
ijassa-899	128	8	a	a	DET
ijassa-899	128	9	distribution	distribution	NOUN
ijassa-899	128	10	of	of	ADP
ijassa-899	128	11	mixture	mixture	NOUN
ijassa-899	128	12	of	of	ADP
ijassa-899	128	13	distributions	distribution	NOUN
ijassa-899	128	14	p̃(a|b	p̃(a|b	PROPN
ijassa-899	128	15	)	)	PUNCT
ijassa-899	128	16	j	j	NOUN
ijassa-899	128	17	with	with	ADP
ijassa-899	128	18	weights	weight	NOUN
ijassa-899	128	19	w̃j	w̃j	PROPN
ijassa-899	128	20	p̃(a|b	p̃(a|b	PROPN
ijassa-899	128	21	)	)	PUNCT
ijassa-899	128	22	=	=	SYM
ijassa-899	129	1	k∑	k∑	NOUN
ijassa-899	129	2	j=1	j=1	PROPN
ijassa-899	129	3	w̃jp̃	w̃jp̃	VERB
ijassa-899	129	4	(	(	PUNCT
ijassa-899	129	5	a|b	a|b	NOUN
ijassa-899	129	6	)	)	PUNCT
ijassa-899	129	7	j	j	PROPN
ijassa-899	129	8	.	.	PUNCT
ijassa-899	130	1	3.2	3.2	NUM
ijassa-899	130	2	.	.	PUNCT
ijassa-899	130	3	normal	normal	ADJ
ijassa-899	130	4	mixture	mixture	NOUN
ijassa-899	130	5	distribution	distribution	NOUN
ijassa-899	130	6	the	the	DET
ijassa-899	130	7	density	density	NOUN
ijassa-899	130	8	in	in	ADP
ijassa-899	130	9	the	the	DET
ijassa-899	130	10	model	model	NOUN
ijassa-899	130	11	of	of	ADP
ijassa-899	130	12	a	a	DET
ijassa-899	130	13	mixture	mixture	NOUN
ijassa-899	130	14	of	of	ADP
ijassa-899	130	15	normal	normal	ADJ
ijassa-899	130	16	distributions	distribution	NOUN
ijassa-899	130	17	equals	equal	VERB
ijassa-899	130	18	p(x	p(x	NOUN
ijassa-899	130	19	)	)	PUNCT
ijassa-899	130	20	=	=	SYM
ijassa-899	130	21	k∑	k∑	PROPN
ijassa-899	131	1	j=1	j=1	PROPN
ijassa-899	131	2	wjq(x|µj	wjq(x|µj	PROPN
ijassa-899	131	3	,	,	PUNCT
ijassa-899	131	4	σj	σj	NOUN
ijassa-899	131	5	)	)	PUNCT
ijassa-899	131	6	.	.	PUNCT
ijassa-899	132	1	let	let	VERB
ijassa-899	132	2	x1	x1	NUM
ijassa-899	132	3	,	,	PUNCT
ijassa-899	132	4	.	.	PUNCT
ijassa-899	132	5	.	.	PUNCT
ijassa-899	133	1	.	.	PUNCT
ijassa-899	134	1	,	,	PUNCT
ijassa-899	134	2	xn	xn	PROPN
ijassa-899	134	3	be	be	AUX
ijassa-899	134	4	a	a	DET
ijassa-899	134	5	sample	sample	NOUN
ijassa-899	134	6	from	from	ADP
ijassa-899	134	7	such	such	DET
ijassa-899	134	8	a	a	DET
ijassa-899	134	9	mixture	mixture	NOUN
ijassa-899	134	10	of	of	ADP
ijassa-899	134	11	distributions	distribution	NOUN
ijassa-899	134	12	.	.	PUNCT
ijassa-899	135	1	the	the	DET
ijassa-899	135	2	estimation	estimation	NOUN
ijassa-899	135	3	of	of	ADP
ijassa-899	135	4	mixture	mixture	NOUN
ijassa-899	135	5	parameters	parameter	NOUN
ijassa-899	135	6	is	be	AUX
ijassa-899	135	7	performed	perform	VERB
ijassa-899	135	8	by	by	ADP
ijassa-899	135	9	solving	solve	VERB
ijassa-899	135	10	the	the	DET
ijassa-899	135	11	problem	problem	NOUN
ijassa-899	135	12	of	of	ADP
ijassa-899	135	13	maximizing	maximize	VERB
ijassa-899	135	14	model	model	NOUN
ijassa-899	135	15	likelihood	likelihood	NOUN
ijassa-899	135	16	using	use	VERB
ijassa-899	135	17	an	an	DET
ijassa-899	135	18	iterative	iterative	NOUN
ijassa-899	136	1	em	em	PRON
ijassa-899	136	2	algorithm	algorithm	NOUN
ijassa-899	136	3	[	[	X
ijassa-899	136	4	6	6	NUM
ijassa-899	136	5	]	]	PUNCT
ijassa-899	136	6	.	.	PUNCT
ijassa-899	137	1	this	this	DET
ijassa-899	137	2	procedure	procedure	NOUN
ijassa-899	137	3	consists	consist	VERB
ijassa-899	137	4	in	in	ADP
ijassa-899	137	5	selecting	select	VERB
ijassa-899	137	6	some	some	DET
ijassa-899	137	7	random	random	ADJ
ijassa-899	137	8	initial	initial	ADJ
ijassa-899	137	9	approximation	approximation	NOUN
ijassa-899	137	10	of	of	ADP
ijassa-899	137	11	the	the	DET
ijassa-899	137	12	parameters	parameter	NOUN
ijassa-899	137	13	and	and	CCONJ
ijassa-899	137	14	then	then	ADV
ijassa-899	137	15	alternating	alternate	VERB
ijassa-899	137	16	two	two	NUM
ijassa-899	137	17	steps	step	NOUN
ijassa-899	137	18	.	.	PUNCT
ijassa-899	138	1	for	for	ADP
ijassa-899	138	2	a	a	DET
ijassa-899	138	3	mixture	mixture	NOUN
ijassa-899	138	4	of	of	ADP
ijassa-899	138	5	normal	normal	ADJ
ijassa-899	138	6	distributions	distribution	NOUN
ijassa-899	138	7	they	they	PRON
ijassa-899	138	8	are	be	AUX
ijassa-899	138	9	conducted	conduct	VERB
ijassa-899	138	10	in	in	ADP
ijassa-899	138	11	the	the	DET
ijassa-899	138	12	following	following	ADJ
ijassa-899	138	13	way	way	NOUN
ijassa-899	138	14	:	:	PUNCT
ijassa-899	138	15	e	e	NOUN
ijassa-899	138	16	-	-	NOUN
ijassa-899	138	17	step	step	NOUN
ijassa-899	138	18	.	.	PUNCT
ijassa-899	139	1	compute	compute	VERB
ijassa-899	139	2	the	the	DET
ijassa-899	139	3	following	follow	VERB
ijassa-899	139	4	auxiliary	auxiliary	ADJ
ijassa-899	139	5	values	value	NOUN
ijassa-899	139	6	rij	rij	ADJ
ijassa-899	139	7	=	=	SYM
ijassa-899	139	8	wjq(xi|µj	wjq(xi|µj	PROPN
ijassa-899	139	9	,	,	PUNCT
ijassa-899	139	10	σj	σj	ADJ
ijassa-899	139	11	)	)	PUNCT
ijassa-899	139	12	k∑	k∑	VERB
ijassa-899	140	1	s=1	s=1	X
ijassa-899	140	2	wsq(xi|µs	wsq(xi|µs	ADJ
ijassa-899	140	3	,	,	PUNCT
ijassa-899	140	4	σs	σs	PROPN
ijassa-899	140	5	)	)	PUNCT
ijassa-899	140	6	.	.	PUNCT
ijassa-899	141	1	rij	rij	PROPN
ijassa-899	141	2	is	be	AUX
ijassa-899	141	3	the	the	DET
ijassa-899	141	4	probability	probability	NOUN
ijassa-899	141	5	that	that	SCONJ
ijassa-899	141	6	the	the	DET
ijassa-899	141	7	random	random	ADJ
ijassa-899	141	8	vector	vector	NOUN
ijassa-899	141	9	xi	xi	X
ijassa-899	141	10	is	be	AUX
ijassa-899	141	11	obtained	obtain	VERB
ijassa-899	141	12	from	from	ADP
ijassa-899	141	13	the	the	DET
ijassa-899	141	14	jth	jth	PROPN
ijassa-899	141	15	component	component	NOUN
ijassa-899	141	16	of	of	ADP
ijassa-899	141	17	the	the	DET
ijassa-899	141	18	mixture	mixture	NOUN
ijassa-899	141	19	at	at	ADP
ijassa-899	141	20	the	the	DET
ijassa-899	141	21	current	current	ADJ
ijassa-899	141	22	approximation	approximation	NOUN
ijassa-899	141	23	of	of	ADP
ijassa-899	141	24	the	the	DET
ijassa-899	141	25	parameters	parameter	NOUN
ijassa-899	141	26	wj	wj	PROPN
ijassa-899	141	27	,	,	PUNCT
ijassa-899	141	28	µj	µj	PROPN
ijassa-899	141	29	,	,	PUNCT
ijassa-899	141	30	σj	σj	VERB
ijassa-899	141	31	.	.	PUNCT
ijassa-899	142	1	m	m	NOUN
ijassa-899	142	2	-	-	NOUN
ijassa-899	142	3	step	step	NOUN
ijassa-899	142	4	.	.	PUNCT
ijassa-899	143	1	compute	compute	VERB
ijassa-899	143	2	a	a	DET
ijassa-899	143	3	new	new	ADJ
ijassa-899	143	4	approximation	approximation	NOUN
ijassa-899	143	5	of	of	ADP
ijassa-899	143	6	parameters	parameter	NOUN
ijassa-899	144	1	wj	wj	X
ijassa-899	144	2	=	=	SYM
ijassa-899	144	3	1	1	NUM
ijassa-899	144	4	n	n	NUM
ijassa-899	144	5	n∑	n∑	NOUN
ijassa-899	144	6	i=1	i=1	PROPN
ijassa-899	144	7	rij	rij	PROPN
ijassa-899	144	8	,	,	PUNCT
ijassa-899	144	9	µj	µj	PROPN
ijassa-899	144	10	=	=	SYM
ijassa-899	144	11	n∑	n∑	PROPN
ijassa-899	144	12	i=1	i=1	PROPN
ijassa-899	144	13	rijxi	rijxi	PROPN
ijassa-899	144	14	/	/	PUNCT
ijassa-899	144	15	n∑	n∑	NOUN
ijassa-899	144	16	i=1	i=1	PROPN
ijassa-899	144	17	rij	rij	ADJ
ijassa-899	144	18	,	,	PUNCT
ijassa-899	144	19	σj	σj	VERB
ijassa-899	144	20	=	=	PUNCT
ijassa-899	144	21	n∑	n∑	NOUN
ijassa-899	144	22	i=1	i=1	PROPN
ijassa-899	145	1	rij(xi	rij(xi	NOUN
ijassa-899	145	2	−	−	PUNCT
ijassa-899	146	1	µj)2	µj)2	PROPN
ijassa-899	146	2	/	/	SYM
ijassa-899	146	3	n∑	n∑	NOUN
ijassa-899	146	4	i=1	i=1	PROPN
ijassa-899	146	5	rij	rij	PROPN
ijassa-899	146	6	.	.	PUNCT
ijassa-899	147	1	stopping	stop	VERB
ijassa-899	147	2	criterion	criterion	NOUN
ijassa-899	147	3	.	.	PUNCT
ijassa-899	148	1	iterations	iteration	NOUN
ijassa-899	148	2	of	of	ADP
ijassa-899	148	3	the	the	DET
ijassa-899	148	4	method	method	NOUN
ijassa-899	148	5	are	be	AUX
ijassa-899	148	6	made	make	VERB
ijassa-899	148	7	up	up	ADP
ijassa-899	148	8	to	to	ADP
ijassa-899	148	9	the	the	DET
ijassa-899	148	10	convergence	convergence	NOUN
ijassa-899	148	11	of	of	ADP
ijassa-899	148	12	the	the	DET
ijassa-899	148	13	variational	variational	ADV
ijassa-899	148	14	lower	lower	ADV
ijassa-899	148	15	bound	bind	VERB
ijassa-899	148	16	on	on	ADP
ijassa-899	148	17	the	the	DET
ijassa-899	148	18	logarithmic	logarithmic	ADJ
ijassa-899	148	19	likelihood	likelihood	NOUN
ijassa-899	148	20	function	function	NOUN
ijassa-899	148	21	l(w	l(w	PROPN
ijassa-899	148	22	,	,	PUNCT
ijassa-899	148	23	µ,σ	µ,σ	INTJ
ijassa-899	148	24	,	,	PUNCT
ijassa-899	148	25	r	r	NOUN
ijassa-899	148	26	)	)	PUNCT
ijassa-899	148	27	=	=	SYM
ijassa-899	149	1	n∑	n∑	PROPN
ijassa-899	149	2	i=1	i=1	PROPN
ijassa-899	150	1	k∑	k∑	PROPN
ijassa-899	151	1	j=1	j=1	PROPN
ijassa-899	151	2	rij	rij	PROPN
ijassa-899	152	1	[	[	X
ijassa-899	152	2	lnwj	lnwj	NOUN
ijassa-899	152	3	+	+	X
ijassa-899	152	4	ln	ln	ADJ
ijassa-899	152	5	q(xi|µj	q(xi|µj	PROPN
ijassa-899	152	6	,	,	PUNCT
ijassa-899	152	7	σj)]−	σj)]−	PROPN
ijassa-899	152	8	n∑	n∑	PROPN
ijassa-899	153	1	i=1	i=1	PROPN
ijassa-899	153	2	k∑	k∑	PROPN
ijassa-899	154	1	j=1	j=1	PROPN
ijassa-899	154	2	rij	rij	PROPN
ijassa-899	154	3	ln	ln	PROPN
ijassa-899	154	4	rij	rij	PROPN
ijassa-899	154	5	.	.	PUNCT
ijassa-899	155	1	the	the	DET
ijassa-899	155	2	procedure	procedure	NOUN
ijassa-899	155	3	terminates	terminate	VERB
ijassa-899	155	4	as	as	ADP
ijassa-899	155	5	l	l	NOUN
ijassa-899	155	6	changes	change	NOUN
ijassa-899	155	7	in	in	ADP
ijassa-899	155	8	no	no	DET
ijassa-899	155	9	more	more	ADJ
ijassa-899	155	10	than	than	ADP
ijassa-899	155	11	a	a	DET
ijassa-899	155	12	pre	pre	ADJ
ijassa-899	155	13	-	-	ADJ
ijassa-899	155	14	set	set	ADJ
ijassa-899	155	15	small	small	ADJ
ijassa-899	155	16	number	number	NOUN
ijassa-899	155	17	ε	ε	PROPN
ijassa-899	155	18	>	>	X
ijassa-899	155	19	0	0	PUNCT
ijassa-899	156	1	[	[	X
ijassa-899	156	2	6	6	NUM
ijassa-899	156	3	]	]	PUNCT
ijassa-899	156	4	.	.	PUNCT
ijassa-899	157	1	copyright	copyright	NOUN
ijassa-899	157	2	©	©	PROPN
ijassa-899	157	3	2020	2020	NUM
ijassa-899	157	4	assa	assa	NOUN
ijassa-899	157	5	.	.	PUNCT
ijassa-899	158	1	adv	adv	PROPN
ijassa-899	158	2	syst	syst	PROPN
ijassa-899	158	3	sci	sci	PROPN
ijassa-899	158	4	appl	appl	PROPN
ijassa-899	158	5	(	(	PUNCT
ijassa-899	158	6	2020	2020	NUM
ijassa-899	158	7	)	)	PUNCT
ijassa-899	158	8	student	student	NOUN
ijassa-899	158	9	mixture	mixture	NOUN
ijassa-899	158	10	and	and	CCONJ
ijassa-899	158	11	its	its	PRON
ijassa-899	158	12	machine	machine	NOUN
ijassa-899	158	13	learning	learn	VERB
ijassa-899	158	14	applications	application	NOUN
ijassa-899	158	15	to	to	ADP
ijassa-899	158	16	pvt	pvt	PROPN
ijassa-899	158	17	properties	property	NOUN
ijassa-899	158	18	103	103	NUM
ijassa-899	158	19	3.3	3.3	NUM
ijassa-899	158	20	.	.	PUNCT
ijassa-899	159	1	student	student	NOUN
ijassa-899	159	2	mixture	mixture	NOUN
ijassa-899	159	3	distribution	distribution	NOUN
ijassa-899	159	4	the	the	DET
ijassa-899	159	5	density	density	NOUN
ijassa-899	159	6	of	of	ADP
ijassa-899	159	7	a	a	DET
ijassa-899	159	8	mixture	mixture	NOUN
ijassa-899	159	9	of	of	ADP
ijassa-899	159	10	multidimensional	multidimensional	ADJ
ijassa-899	159	11	student	student	NOUN
ijassa-899	159	12	distributions	distribution	NOUN
ijassa-899	159	13	equals	equal	VERB
ijassa-899	159	14	p(x	p(x	NOUN
ijassa-899	159	15	)	)	PUNCT
ijassa-899	159	16	=	=	PUNCT
ijassa-899	159	17	k∑	k∑	PROPN
ijassa-899	160	1	j=1	j=1	PROPN
ijassa-899	160	2	wjp(x|µj	wjp(x|µj	ADV
ijassa-899	160	3	,	,	PUNCT
ijassa-899	160	4	σj	σj	NOUN
ijassa-899	160	5	,	,	PUNCT
ijassa-899	160	6	ν	ν	NOUN
ijassa-899	160	7	)	)	PUNCT
ijassa-899	160	8	,	,	PUNCT
ijassa-899	160	9	where	where	SCONJ
ijassa-899	160	10	p(x|µj	p(x|µj	NOUN
ijassa-899	160	11	,	,	PUNCT
ijassa-899	160	12	σj	σj	NOUN
ijassa-899	160	13	,	,	PUNCT
ijassa-899	160	14	ν	ν	X
ijassa-899	160	15	)	)	PUNCT
ijassa-899	160	16	is	be	AUX
ijassa-899	160	17	the	the	DET
ijassa-899	160	18	density	density	NOUN
ijassa-899	160	19	of	of	ADP
ijassa-899	160	20	multidimensional	multidimensional	ADJ
ijassa-899	160	21	student	student	NOUN
ijassa-899	160	22	distribution	distribution	NOUN
ijassa-899	160	23	with	with	ADP
ijassa-899	160	24	ν	ν	X
ijassa-899	160	25	degrees	degree	NOUN
ijassa-899	160	26	of	of	ADP
ijassa-899	160	27	freedom	freedom	NOUN
ijassa-899	160	28	centered	center	VERB
ijassa-899	160	29	at	at	ADP
ijassa-899	160	30	µj	µj	PROPN
ijassa-899	160	31	and	and	CCONJ
ijassa-899	160	32	the	the	DET
ijassa-899	160	33	scale	scale	NOUN
ijassa-899	160	34	matrix	matrix	NOUN
ijassa-899	160	35	σj	σj	VERB
ijassa-899	160	36	.	.	PUNCT
ijassa-899	161	1	parameter	parameter	PROPN
ijassa-899	161	2	ν	ν	PROPN
ijassa-899	161	3	is	be	AUX
ijassa-899	161	4	a	a	DET
ijassa-899	161	5	hyperparameter	hyperparameter	NOUN
ijassa-899	161	6	of	of	ADP
ijassa-899	161	7	the	the	DET
ijassa-899	161	8	model	model	NOUN
ijassa-899	161	9	.	.	PUNCT
ijassa-899	162	1	let	let	VERB
ijassa-899	162	2	x	x	PUNCT
ijassa-899	162	3	=	=	SYM
ijassa-899	162	4	(	(	PUNCT
ijassa-899	162	5	x1	x1	PROPN
ijassa-899	162	6	,	,	PUNCT
ijassa-899	162	7	.	.	PUNCT
ijassa-899	162	8	.	.	PUNCT
ijassa-899	162	9	.	.	PUNCT
ijassa-899	163	1	,	,	PUNCT
ijassa-899	163	2	xn	xn	X
ijassa-899	163	3	)	)	PUNCT
ijassa-899	163	4	be	be	VERB
ijassa-899	163	5	sample	sample	NOUN
ijassa-899	163	6	vectors	vector	NOUN
ijassa-899	163	7	from	from	ADP
ijassa-899	163	8	that	that	DET
ijassa-899	163	9	mixture	mixture	NOUN
ijassa-899	163	10	distribution	distribution	NOUN
ijassa-899	163	11	.	.	PUNCT
ijassa-899	164	1	the	the	DET
ijassa-899	164	2	proposed	propose	VERB
ijassa-899	164	3	method	method	NOUN
ijassa-899	164	4	for	for	ADP
ijassa-899	164	5	estimating	estimate	VERB
ijassa-899	164	6	the	the	DET
ijassa-899	164	7	parameters	parameter	NOUN
ijassa-899	164	8	of	of	ADP
ijassa-899	164	9	the	the	DET
ijassa-899	164	10	mixture	mixture	NOUN
ijassa-899	164	11	consists	consist	VERB
ijassa-899	164	12	in	in	ADP
ijassa-899	164	13	selecting	select	VERB
ijassa-899	164	14	some	some	DET
ijassa-899	164	15	initial	initial	ADJ
ijassa-899	164	16	approximation	approximation	NOUN
ijassa-899	164	17	of	of	ADP
ijassa-899	164	18	the	the	DET
ijassa-899	164	19	parameters	parameter	NOUN
ijassa-899	164	20	and	and	CCONJ
ijassa-899	164	21	performing	perform	VERB
ijassa-899	164	22	the	the	DET
ijassa-899	164	23	next	next	ADJ
ijassa-899	164	24	steps	step	NOUN
ijassa-899	164	25	at	at	ADP
ijassa-899	164	26	each	each	DET
ijassa-899	164	27	iteration	iteration	NOUN
ijassa-899	164	28	.	.	PUNCT
ijassa-899	165	1	e	e	X
ijassa-899	165	2	-	-	NOUN
ijassa-899	165	3	step	step	NOUN
ijassa-899	165	4	.	.	PUNCT
ijassa-899	166	1	perform	perform	VERB
ijassa-899	166	2	several	several	ADJ
ijassa-899	166	3	iterations	iteration	NOUN
ijassa-899	166	4	of	of	ADP
ijassa-899	166	5	the	the	DET
ijassa-899	166	6	next	next	ADJ
ijassa-899	166	7	two	two	NUM
ijassa-899	166	8	steps	step	NOUN
ijassa-899	166	9	:	:	PUNCT
ijassa-899	166	10	i.	i.	NOUN
ijassa-899	166	11	compute	compute	VERB
ijassa-899	166	12	the	the	DET
ijassa-899	166	13	following	follow	VERB
ijassa-899	166	14	auxiliary	auxiliary	ADJ
ijassa-899	166	15	values	value	NOUN
ijassa-899	166	16	rij	rij	ADJ
ijassa-899	166	17	=	=	PUNCT
ijassa-899	166	18	wjq	wjq	NOUN
ijassa-899	166	19	(	(	PUNCT
ijassa-899	166	20	xi	xi	ADP
ijassa-899	166	21	|µj	|µj	PROPN
ijassa-899	166	22	,	,	PUNCT
ijassa-899	166	23	σjai	σjai	NOUN
ijassa-899	166	24	/	/	SYM
ijassa-899	166	25	bi	bi	NOUN
ijassa-899	166	26	)	)	PUNCT
ijassa-899	166	27	k∑	k∑	PROPN
ijassa-899	167	1	s=1	s=1	X
ijassa-899	167	2	wsq	wsq	NOUN
ijassa-899	167	3	(	(	PUNCT
ijassa-899	167	4	xi	xi	X
ijassa-899	167	5	|µs	|µs	PROPN
ijassa-899	167	6	,	,	PUNCT
ijassa-899	167	7	σsai	σsai	PROPN
ijassa-899	167	8	/	/	SYM
ijassa-899	167	9	bi	bi	NOUN
ijassa-899	167	10	)	)	PUNCT
ijassa-899	167	11	,	,	PUNCT
ijassa-899	167	12	as	as	ADP
ijassa-899	167	13	above	above	ADV
ijassa-899	167	14	,	,	PUNCT
ijassa-899	167	15	rij	rij	X
ijassa-899	167	16	is	be	AUX
ijassa-899	167	17	the	the	DET
ijassa-899	167	18	probability	probability	NOUN
ijassa-899	167	19	that	that	SCONJ
ijassa-899	167	20	the	the	DET
ijassa-899	167	21	object	object	NOUN
ijassa-899	167	22	xi	xi	X
ijassa-899	167	23	is	be	AUX
ijassa-899	167	24	obtained	obtain	VERB
ijassa-899	167	25	from	from	ADP
ijassa-899	167	26	the	the	DET
ijassa-899	167	27	jth	jth	PROPN
ijassa-899	167	28	component	component	NOUN
ijassa-899	167	29	of	of	ADP
ijassa-899	167	30	the	the	DET
ijassa-899	167	31	mixture	mixture	NOUN
ijassa-899	167	32	at	at	ADP
ijassa-899	167	33	the	the	DET
ijassa-899	167	34	current	current	ADJ
ijassa-899	167	35	approximation	approximation	NOUN
ijassa-899	167	36	of	of	ADP
ijassa-899	167	37	the	the	DET
ijassa-899	167	38	parameterswj	parameterswj	NOUN
ijassa-899	167	39	,	,	PUNCT
ijassa-899	167	40	µj	µj	PROPN
ijassa-899	167	41	,	,	PUNCT
ijassa-899	167	42	σj	σj	PROPN
ijassa-899	167	43	.	.	PUNCT
ijassa-899	168	1	ii	ii	X
ijassa-899	168	2	.	.	PUNCT
ijassa-899	168	3	compute	compute	PROPN
ijassa-899	168	4	ai	ai	NOUN
ijassa-899	168	5	=	=	PUNCT
ijassa-899	168	6	ν	ν	NOUN
ijassa-899	168	7	+	+	CCONJ
ijassa-899	168	8	d	d	PROPN
ijassa-899	168	9	2	2	NUM
ijassa-899	168	10	,	,	PUNCT
ijassa-899	168	11	bi	bi	NOUN
ijassa-899	168	12	=	=	NOUN
ijassa-899	168	13	ν	ν	NOUN
ijassa-899	168	14	2	2	NUM
ijassa-899	168	15	+	+	CCONJ
ijassa-899	168	16	1	1	NUM
ijassa-899	168	17	2	2	NUM
ijassa-899	168	18	k∑	k∑	NOUN
ijassa-899	168	19	j=1	j=1	PROPN
ijassa-899	168	20	rij	rij	PROPN
ijassa-899	168	21	(	(	PUNCT
ijassa-899	168	22	xi	xi	X
ijassa-899	168	23	−	−	PROPN
ijassa-899	168	24	µj)tς−1	µj)tς−1	PROPN
ijassa-899	168	25	j	j	PROPN
ijassa-899	168	26	(	(	PUNCT
ijassa-899	168	27	xi	xi	PROPN
ijassa-899	168	28	−	−	PROPN
ijassa-899	168	29	µj	µj	PROPN
ijassa-899	168	30	)	)	PUNCT
ijassa-899	168	31	,	,	PUNCT
ijassa-899	168	32	ci	ci	NOUN
ijassa-899	168	33	=	=	SYM
ijassa-899	168	34	bi	bi	PROPN
ijassa-899	168	35	/	/	SYM
ijassa-899	168	36	ai	ai	NOUN
ijassa-899	168	37	,	,	PUNCT
ijassa-899	168	38	where	where	SCONJ
ijassa-899	168	39	d	d	NOUN
ijassa-899	168	40	is	be	AUX
ijassa-899	168	41	the	the	DET
ijassa-899	168	42	dimension	dimension	NOUN
ijassa-899	168	43	of	of	ADP
ijassa-899	168	44	the	the	DET
ijassa-899	168	45	feature	feature	NOUN
ijassa-899	168	46	space	space	NOUN
ijassa-899	168	47	.	.	PUNCT
ijassa-899	169	1	m	m	NOUN
ijassa-899	169	2	-	-	PUNCT
ijassa-899	169	3	step	step	NOUN
ijassa-899	169	4	.	.	PUNCT
ijassa-899	170	1	compute	compute	VERB
ijassa-899	170	2	a	a	DET
ijassa-899	170	3	new	new	ADJ
ijassa-899	170	4	approximation	approximation	NOUN
ijassa-899	170	5	of	of	ADP
ijassa-899	170	6	parameters	parameter	NOUN
ijassa-899	171	1	wj	wj	X
ijassa-899	171	2	=	=	PUNCT
ijassa-899	171	3	n∑	n∑	PROPN
ijassa-899	171	4	i=1	i=1	PROPN
ijassa-899	172	1	rij	rij	PROPN
ijassa-899	172	2	/	/	SYM
ijassa-899	172	3	n	n	CCONJ
ijassa-899	172	4	,	,	PUNCT
ijassa-899	172	5	k∑	k∑	VERB
ijassa-899	173	1	i	i	PRON
ijassa-899	173	2	,	,	PUNCT
ijassa-899	173	3	j=1	j=1	PROPN
ijassa-899	173	4	rij	rij	X
ijassa-899	173	5	,	,	PUNCT
ijassa-899	173	6	µj	µj	PROPN
ijassa-899	174	1	=	=	SYM
ijassa-899	174	2	n∑	n∑	PROPN
ijassa-899	174	3	i=1	i=1	PROPN
ijassa-899	175	1	rijci	rijci	PROPN
ijassa-899	175	2	xi	xi	PROPN
ijassa-899	175	3	/	/	SYM
ijassa-899	175	4	n∑	n∑	PROPN
ijassa-899	175	5	i=1	i=1	PROPN
ijassa-899	175	6	rijci	rijci	PROPN
ijassa-899	175	7	,	,	PUNCT
ijassa-899	175	8	σj	σj	VERB
ijassa-899	175	9	=	=	SYM
ijassa-899	175	10	1	1	NUM
ijassa-899	175	11	n	n	NUM
ijassa-899	175	12	n∑	n∑	PROPN
ijassa-899	175	13	i=1	i=1	PROPN
ijassa-899	175	14	ci	ci	PROPN
ijassa-899	176	1	(	(	PUNCT
ijassa-899	176	2	xi	xi	X
ijassa-899	176	3	−	−	PROPN
ijassa-899	176	4	µj)(xi	µj)(xi	PUNCT
ijassa-899	176	5	−	−	PROPN
ijassa-899	176	6	µj)t	µj)t	PRON
ijassa-899	176	7	.	.	PUNCT
ijassa-899	177	1	stopping	stop	VERB
ijassa-899	177	2	criterion	criterion	NOUN
ijassa-899	177	3	.	.	PUNCT
ijassa-899	178	1	iterations	iteration	NOUN
ijassa-899	178	2	of	of	ADP
ijassa-899	178	3	the	the	DET
ijassa-899	178	4	method	method	NOUN
ijassa-899	178	5	are	be	AUX
ijassa-899	178	6	performed	perform	VERB
ijassa-899	178	7	up	up	ADP
ijassa-899	178	8	to	to	ADP
ijassa-899	178	9	convergence	convergence	NOUN
ijassa-899	178	10	of	of	ADP
ijassa-899	178	11	l(w	l(w	PROPN
ijassa-899	178	12	,	,	PUNCT
ijassa-899	178	13	µ,σ	µ,σ	INTJ
ijassa-899	178	14	,	,	PUNCT
ijassa-899	178	15	r	r	NOUN
ijassa-899	178	16	,	,	PUNCT
ijassa-899	178	17	a	a	DET
ijassa-899	178	18	,	,	PUNCT
ijassa-899	178	19	b	b	NOUN
ijassa-899	178	20	)	)	PUNCT
ijassa-899	179	1	=	=	SYM
ijassa-899	179	2	n∑	n∑	PROPN
ijassa-899	179	3	i=1	i=1	PROPN
ijassa-899	180	1	k∑	k∑	PROPN
ijassa-899	181	1	j=1	j=1	PROPN
ijassa-899	181	2	rij	rij	X
ijassa-899	181	3	[	[	PUNCT
ijassa-899	181	4	lnwj	lnwj	NOUN
ijassa-899	181	5	−	−	PROPN
ijassa-899	182	1	d	d	PROPN
ijassa-899	182	2	2	2	NUM
ijassa-899	182	3	ln	ln	NOUN
ijassa-899	182	4	2π	2π	NOUN
ijassa-899	182	5	−	−	NOUN
ijassa-899	182	6	1	1	NUM
ijassa-899	182	7	2	2	NUM
ijassa-899	182	8	ln	ln	NOUN
ijassa-899	182	9	det	det	X
ijassa-899	182	10	σj−	σj−	PUNCT
ijassa-899	182	11	−	−	PROPN
ijassa-899	182	12	bi	bi	NOUN
ijassa-899	182	13	2ai	2ai	NOUN
ijassa-899	182	14	[	[	PUNCT
ijassa-899	182	15	ν	ν	X
ijassa-899	182	16	+	+	CCONJ
ijassa-899	182	17	(	(	PUNCT
ijassa-899	182	18	xi	xi	INTJ
ijassa-899	182	19	−	−	PROPN
ijassa-899	182	20	µj)tς−1	µj)tς−1	PROPN
ijassa-899	182	21	j	j	PROPN
ijassa-899	182	22	(	(	PUNCT
ijassa-899	182	23	xi	xi	PROPN
ijassa-899	182	24	−	−	PROPN
ijassa-899	182	25	µj	µj	PROPN
ijassa-899	182	26	)	)	PUNCT
ijassa-899	182	27	]	]	PUNCT
ijassa-899	183	1	+	+	CCONJ
ijassa-899	183	2	ν	ν	X
ijassa-899	183	3	2	2	NUM
ijassa-899	183	4	ln	ln	NOUN
ijassa-899	183	5	ν	ν	NOUN
ijassa-899	183	6	2	2	NUM
ijassa-899	183	7	−	−	NOUN
ijassa-899	183	8	γ	γ	NOUN
ijassa-899	183	9	(	(	PUNCT
ijassa-899	183	10	ν	ν	PROPN
ijassa-899	183	11	2	2	NUM
ijassa-899	183	12	)	)	PUNCT
ijassa-899	183	13	+	+	CCONJ
ijassa-899	183	14	(	(	PUNCT
ijassa-899	183	15	ν	ν	X
ijassa-899	183	16	+	+	CCONJ
ijassa-899	183	17	d	d	SYM
ijassa-899	183	18	2	2	NUM
ijassa-899	183	19	−	−	NOUN
ijassa-899	183	20	1	1	NUM
ijassa-899	183	21	)	)	PUNCT
ijassa-899	183	22	(	(	PUNCT
ijassa-899	183	23	ψ(bi)−	ψ(bi)−	NUM
ijassa-899	183	24	ln	ln	PROPN
ijassa-899	183	25	ai	ai	NOUN
ijassa-899	183	26	)	)	PUNCT
ijassa-899	183	27	]	]	PUNCT
ijassa-899	184	1	−	−	PROPN
ijassa-899	184	2	−	−	PROPN
ijassa-899	185	1	n∑	n∑	PROPN
ijassa-899	185	2	i=1	i=1	PROPN
ijassa-899	185	3	k∑	k∑	PROPN
ijassa-899	186	1	j=1	j=1	PROPN
ijassa-899	186	2	rij	rij	PROPN
ijassa-899	186	3	ln	ln	PROPN
ijassa-899	186	4	rij	rij	PROPN
ijassa-899	186	5	−	−	PROPN
ijassa-899	187	1	n∑	n∑	NOUN
ijassa-899	187	2	i=1	i=1	X
ijassa-899	188	1	[	[	X
ijassa-899	188	2	bi	bi	NOUN
ijassa-899	188	3	ln	ln	NOUN
ijassa-899	188	4	ai	ai	PROPN
ijassa-899	188	5	−	−	PROPN
ijassa-899	188	6	ln	ln	ADJ
ijassa-899	188	7	γ(bi	γ(bi	PROPN
ijassa-899	188	8	)	)	PUNCT
ijassa-899	189	1	+	+	CCONJ
ijassa-899	189	2	(	(	PUNCT
ijassa-899	189	3	bi	bi	ADJ
ijassa-899	189	4	−	−	PROPN
ijassa-899	189	5	1	1	NUM
ijassa-899	189	6	)	)	PUNCT
ijassa-899	189	7	(	(	PUNCT
ijassa-899	189	8	ψ(bi)−	ψ(bi)−	X
ijassa-899	189	9	ln	ln	ADJ
ijassa-899	189	10	ai)−	ai)−	NOUN
ijassa-899	189	11	bi	bi	NOUN
ijassa-899	189	12	]	]	PUNCT
ijassa-899	189	13	.	.	PUNCT
ijassa-899	190	1	copyright	copyright	NOUN
ijassa-899	190	2	©	©	PROPN
ijassa-899	190	3	2020	2020	NUM
ijassa-899	190	4	assa	assa	NOUN
ijassa-899	190	5	.	.	PUNCT
ijassa-899	191	1	adv	adv	PROPN
ijassa-899	191	2	syst	syst	PROPN
ijassa-899	191	3	sci	sci	PROPN
ijassa-899	191	4	appl	appl	PROPN
ijassa-899	191	5	(	(	PUNCT
ijassa-899	191	6	2020	2020	NUM
ijassa-899	191	7	)	)	PUNCT
ijassa-899	191	8	104	104	NUM
ijassa-899	191	9	n.a	n.a	PROPN
ijassa-899	191	10	.	.	PROPN
ijassa-899	191	11	volkov	volkov	PROPN
ijassa-899	191	12	,	,	PUNCT
ijassa-899	191	13	e.yu	e.yu	PROPN
ijassa-899	191	14	.	.	PROPN
ijassa-899	191	15	dakhova	dakhova	PROPN
ijassa-899	191	16	,	,	PUNCT
ijassa-899	191	17	s.a	s.a	PROPN
ijassa-899	191	18	.	.	PROPN
ijassa-899	191	19	budennyy	budennyy	PROPN
ijassa-899	191	20	,	,	PUNCT
ijassa-899	191	21	a.m.	a.m.	PROPN
ijassa-899	191	22	andrianova	andrianova	X
ijassa-899	191	23	using	use	VERB
ijassa-899	191	24	those	those	DET
ijassa-899	191	25	iterations	iteration	NOUN
ijassa-899	191	26	,	,	PUNCT
ijassa-899	191	27	the	the	DET
ijassa-899	191	28	method	method	NOUN
ijassa-899	191	29	approximates	approximate	VERB
ijassa-899	191	30	a	a	DET
ijassa-899	191	31	local	local	ADJ
ijassa-899	191	32	maximum	maximum	NOUN
ijassa-899	191	33	of	of	ADP
ijassa-899	191	34	the	the	DET
ijassa-899	191	35	logarithmic	logarithmic	ADJ
ijassa-899	191	36	likelihood	likelihood	NOUN
ijassa-899	191	37	function	function	NOUN
ijassa-899	191	38	,	,	PUNCT
ijassa-899	191	39	which	which	PRON
ijassa-899	191	40	makes	make	VERB
ijassa-899	191	41	it	it	PRON
ijassa-899	191	42	necessary	necessary	ADJ
ijassa-899	191	43	to	to	PART
ijassa-899	191	44	run	run	VERB
ijassa-899	191	45	the	the	DET
ijassa-899	191	46	procedure	procedure	NOUN
ijassa-899	191	47	several	several	ADJ
ijassa-899	191	48	times	time	NOUN
ijassa-899	191	49	from	from	ADP
ijassa-899	191	50	different	different	ADJ
ijassa-899	191	51	initial	initial	ADJ
ijassa-899	191	52	points	point	NOUN
ijassa-899	191	53	.	.	PUNCT
ijassa-899	192	1	for	for	ADP
ijassa-899	192	2	more	more	ADJ
ijassa-899	192	3	information	information	NOUN
ijassa-899	192	4	about	about	ADP
ijassa-899	192	5	method	method	NOUN
ijassa-899	192	6	convergence	convergence	NOUN
ijassa-899	192	7	see	see	VERB
ijassa-899	192	8	section	section	NOUN
ijassa-899	192	9	4	4	NUM
ijassa-899	192	10	.	.	PUNCT
ijassa-899	193	1	the	the	DET
ijassa-899	193	2	hyperparameter	hyperparameter	NOUN
ijassa-899	193	3	ν	ν	NOUN
ijassa-899	193	4	can	can	AUX
ijassa-899	193	5	be	be	AUX
ijassa-899	193	6	chosen	choose	VERB
ijassa-899	193	7	in	in	ADP
ijassa-899	193	8	a	a	DET
ijassa-899	193	9	usual	usual	ADJ
ijassa-899	193	10	way	way	NOUN
ijassa-899	193	11	by	by	ADP
ijassa-899	193	12	performing	perform	VERB
ijassa-899	193	13	the	the	DET
ijassa-899	193	14	algorithm	algorithm	NOUN
ijassa-899	193	15	for	for	ADP
ijassa-899	193	16	several	several	ADJ
ijassa-899	193	17	values	value	NOUN
ijassa-899	193	18	ν	ν	X
ijassa-899	193	19	and	and	CCONJ
ijassa-899	193	20	choosing	choose	VERB
ijassa-899	193	21	the	the	DET
ijassa-899	193	22	one	one	NOUN
ijassa-899	193	23	that	that	PRON
ijassa-899	193	24	maximizes	maximize	VERB
ijassa-899	193	25	l.	l.	PROPN
ijassa-899	193	26	4	4	NUM
ijassa-899	193	27	.	.	PUNCT
ijassa-899	194	1	formulas	formula	NOUN
ijassa-899	194	2	derivation	derivation	NOUN
ijassa-899	194	3	for	for	ADP
ijassa-899	194	4	estimation	estimation	NOUN
ijassa-899	194	5	the	the	DET
ijassa-899	194	6	parameters	parameter	NOUN
ijassa-899	194	7	of	of	ADP
ijassa-899	194	8	student	student	NOUN
ijassa-899	194	9	mixture	mixture	NOUN
ijassa-899	194	10	let	let	VERB
ijassa-899	194	11	x	x	PUNCT
ijassa-899	194	12	=	=	SYM
ijassa-899	194	13	(	(	PUNCT
ijassa-899	194	14	x1	x1	PROPN
ijassa-899	194	15	,	,	PUNCT
ijassa-899	194	16	...	...	PUNCT
ijassa-899	194	17	,	,	PUNCT
ijassa-899	194	18	xn	xn	X
ijassa-899	194	19	)	)	PUNCT
ijassa-899	194	20	be	be	VERB
ijassa-899	194	21	a	a	DET
ijassa-899	194	22	sample	sample	NOUN
ijassa-899	194	23	of	of	ADP
ijassa-899	194	24	vectors	vector	NOUN
ijassa-899	194	25	from	from	ADP
ijassa-899	194	26	a	a	DET
ijassa-899	194	27	mixture	mixture	NOUN
ijassa-899	194	28	of	of	ADP
ijassa-899	194	29	student	student	NOUN
ijassa-899	194	30	distributions	distribution	NOUN
ijassa-899	194	31	.	.	PUNCT
ijassa-899	195	1	the	the	DET
ijassa-899	195	2	likelihood	likelihood	NOUN
ijassa-899	195	3	equals	equal	VERB
ijassa-899	195	4	l(w	l(w	NOUN
ijassa-899	195	5	,	,	PUNCT
ijassa-899	195	6	µ,σ	µ,σ	NOUN
ijassa-899	195	7	)	)	PUNCT
ijassa-899	196	1	=	=	PUNCT
ijassa-899	196	2	n∏	n∏	PROPN
ijassa-899	196	3	i=1	i=1	PROPN
ijassa-899	196	4	k∑	k∑	PROPN
ijassa-899	196	5	j=1	j=1	PROPN
ijassa-899	196	6	wjp(xi|µj	wjp(xi|µj	PROPN
ijassa-899	196	7	,	,	PUNCT
ijassa-899	196	8	σj	σj	NOUN
ijassa-899	196	9	,	,	PUNCT
ijassa-899	196	10	ν	ν	NOUN
ijassa-899	196	11	)	)	PUNCT
ijassa-899	196	12	.	.	PUNCT
ijassa-899	197	1	the	the	DET
ijassa-899	197	2	family	family	NOUN
ijassa-899	197	3	of	of	ADP
ijassa-899	197	4	distributions	distribution	NOUN
ijassa-899	197	5	does	do	AUX
ijassa-899	197	6	not	not	PART
ijassa-899	197	7	belong	belong	VERB
ijassa-899	197	8	to	to	ADP
ijassa-899	197	9	the	the	DET
ijassa-899	197	10	exponential	exponential	ADJ
ijassa-899	197	11	class	class	NOUN
ijassa-899	197	12	of	of	ADP
ijassa-899	197	13	distributions	distribution	NOUN
ijassa-899	197	14	.	.	PUNCT
ijassa-899	198	1	therefore	therefore	ADV
ijassa-899	198	2	,	,	PUNCT
ijassa-899	198	3	a	a	DET
ijassa-899	198	4	maximization	maximization	NOUN
ijassa-899	198	5	of	of	ADP
ijassa-899	198	6	likelihood	likelihood	NOUN
ijassa-899	198	7	is	be	AUX
ijassa-899	198	8	not	not	PART
ijassa-899	198	9	straightforward	straightforward	ADJ
ijassa-899	198	10	.	.	PUNCT
ijassa-899	199	1	let	let	VERB
ijassa-899	199	2	us	we	PRON
ijassa-899	199	3	use	use	VERB
ijassa-899	199	4	the	the	DET
ijassa-899	199	5	representation	representation	NOUN
ijassa-899	199	6	of	of	ADP
ijassa-899	199	7	a	a	DET
ijassa-899	199	8	student	student	NOUN
ijassa-899	199	9	random	random	ADJ
ijassa-899	199	10	vector	vector	NOUN
ijassa-899	199	11	via	via	ADP
ijassa-899	199	12	a	a	DET
ijassa-899	199	13	normal	normal	ADJ
ijassa-899	199	14	random	random	ADJ
ijassa-899	199	15	vector	vector	NOUN
ijassa-899	199	16	and	and	CCONJ
ijassa-899	199	17	a	a	DET
ijassa-899	199	18	gamma	gamma	NOUN
ijassa-899	199	19	random	random	ADJ
ijassa-899	199	20	vector	vector	NOUN
ijassa-899	199	21	.	.	PUNCT
ijassa-899	200	1	next	next	ADV
ijassa-899	200	2	,	,	PUNCT
ijassa-899	200	3	we	we	PRON
ijassa-899	200	4	introduce	introduce	VERB
ijassa-899	200	5	hidden	hidden	ADJ
ijassa-899	200	6	(	(	PUNCT
ijassa-899	200	7	i.e.	i.e.	X
ijassa-899	200	8	unknown	unknown	ADJ
ijassa-899	200	9	)	)	PUNCT
ijassa-899	200	10	values	value	NOUN
ijassa-899	200	11	.	.	PUNCT
ijassa-899	201	1	1	1	X
ijassa-899	201	2	.	.	X
ijassa-899	201	3	for	for	ADP
ijassa-899	201	4	each	each	DET
ijassa-899	201	5	xi	xi	PROPN
ijassa-899	201	6	,	,	PUNCT
ijassa-899	201	7	introduce	introduce	VERB
ijassa-899	201	8	the	the	DET
ijassa-899	201	9	cluster	cluster	NOUN
ijassa-899	201	10	number	number	NOUN
ijassa-899	201	11	as	as	ADP
ijassa-899	201	12	ti	ti	X
ijassa-899	201	13	=	=	SYM
ijassa-899	201	14	(	(	PUNCT
ijassa-899	201	15	ti1	ti1	PROPN
ijassa-899	201	16	,	,	PUNCT
ijassa-899	201	17	.	.	PUNCT
ijassa-899	201	18	.	.	PUNCT
ijassa-899	202	1	.	.	PUNCT
ijassa-899	203	1	,	,	PUNCT
ijassa-899	203	2	tik	tik	NOUN
ijassa-899	203	3	)	)	PUNCT
ijassa-899	203	4	∈	∈	PROPN
ijassa-899	203	5	{	{	PUNCT
ijassa-899	203	6	0	0	NUM
ijassa-899	203	7	,	,	PUNCT
ijassa-899	203	8	1}k	1}k	NUM
ijassa-899	203	9	,	,	PUNCT
ijassa-899	203	10	with∑k	with∑k	X
ijassa-899	203	11	j=1	j=1	PROPN
ijassa-899	203	12	tij	tij	PROPN
ijassa-899	203	13	=	=	NOUN
ijassa-899	203	14	1	1	X
ijassa-899	203	15	.	.	PUNCT
ijassa-899	204	1	the	the	DET
ijassa-899	204	2	value	value	NOUN
ijassa-899	204	3	tij	tij	NOUN
ijassa-899	204	4	=	=	NOUN
ijassa-899	204	5	1	1	NUM
ijassa-899	204	6	if	if	SCONJ
ijassa-899	204	7	the	the	DET
ijassa-899	204	8	object	object	NOUN
ijassa-899	204	9	xi	xi	VERB
ijassa-899	204	10	is	be	AUX
ijassa-899	204	11	taken	take	VERB
ijassa-899	204	12	from	from	ADP
ijassa-899	204	13	the	the	DET
ijassa-899	204	14	cluster	cluster	NOUN
ijassa-899	204	15	j	j	PROPN
ijassa-899	204	16	and	and	CCONJ
ijassa-899	204	17	tij	tij	PROPN
ijassa-899	204	18	=	=	SYM
ijassa-899	204	19	0	0	PUNCT
ijassa-899	205	1	otherwise	otherwise	ADV
ijassa-899	205	2	.	.	PUNCT
ijassa-899	206	1	denote	denote	VERB
ijassa-899	206	2	t	t	PROPN
ijassa-899	206	3	=	=	SYM
ijassa-899	206	4	(	(	PUNCT
ijassa-899	206	5	t1	t1	NOUN
ijassa-899	206	6	,	,	PUNCT
ijassa-899	206	7	.	.	PUNCT
ijassa-899	206	8	.	.	PUNCT
ijassa-899	207	1	.	.	PUNCT
ijassa-899	208	1	,	,	PUNCT
ijassa-899	208	2	tn	tn	PROPN
ijassa-899	208	3	)	)	PUNCT
ijassa-899	208	4	.	.	PUNCT
ijassa-899	209	1	2	2	X
ijassa-899	209	2	.	.	X
ijassa-899	209	3	also	also	ADV
ijassa-899	209	4	for	for	ADP
ijassa-899	209	5	each	each	DET
ijassa-899	209	6	xi	xi	PROPN
ijassa-899	209	7	,	,	PUNCT
ijassa-899	209	8	we	we	PRON
ijassa-899	209	9	introduce	introduce	VERB
ijassa-899	209	10	a	a	DET
ijassa-899	209	11	random	random	ADJ
ijassa-899	209	12	variable	variable	NOUN
ijassa-899	209	13	yi	yi	NOUN
ijassa-899	209	14	that	that	PRON
ijassa-899	209	15	has	have	VERB
ijassa-899	209	16	distribution	distribution	NOUN
ijassa-899	209	17	γ(ν/2	γ(ν/2	PROPN
ijassa-899	209	18	,	,	PUNCT
ijassa-899	209	19	ν/2	ν/2	NUM
ijassa-899	209	20	)	)	PUNCT
ijassa-899	209	21	such	such	ADJ
ijassa-899	209	22	that	that	DET
ijassa-899	209	23	xi	xi	PROPN
ijassa-899	209	24	=	=	PUNCT
ijassa-899	209	25	k∑	k∑	PROPN
ijassa-899	210	1	j=1	j=1	NOUN
ijassa-899	210	2	(	(	PUNCT
ijassa-899	210	3	µj	µj	X
ijassa-899	210	4	+	+	NUM
ijassa-899	210	5	ξij	ξij	NOUN
ijassa-899	210	6	/√	/√	SYM
ijassa-899	210	7	yi	yi	PROPN
ijassa-899	210	8	)	)	PUNCT
ijassa-899	210	9	i{tij	i{tij	PROPN
ijassa-899	211	1	=	=	NOUN
ijassa-899	211	2	1	1	NUM
ijassa-899	211	3	}	}	PUNCT
ijassa-899	211	4	,	,	PUNCT
ijassa-899	211	5	where	where	SCONJ
ijassa-899	211	6	the	the	DET
ijassa-899	211	7	random	random	ADJ
ijassa-899	211	8	vector	vector	NOUN
ijassa-899	211	9	ξij	ξij	NOUN
ijassa-899	211	10	has	have	VERB
ijassa-899	211	11	distribution	distribution	NOUN
ijassa-899	211	12	n	n	CCONJ
ijassa-899	211	13	(	(	PUNCT
ijassa-899	211	14	0	0	NUM
ijassa-899	211	15	,	,	PUNCT
ijassa-899	211	16	σj	σj	VERB
ijassa-899	211	17	)	)	PUNCT
ijassa-899	211	18	and	and	CCONJ
ijassa-899	211	19	is	be	AUX
ijassa-899	211	20	independent	independent	ADJ
ijassa-899	211	21	from	from	ADP
ijassa-899	211	22	yi	yi	PROPN
ijassa-899	211	23	.	.	PUNCT
ijassa-899	212	1	denote	denote	VERB
ijassa-899	212	2	y	y	PROPN
ijassa-899	212	3	=	=	SYM
ijassa-899	212	4	(	(	PUNCT
ijassa-899	212	5	y1	y1	PROPN
ijassa-899	212	6	,	,	PUNCT
ijassa-899	212	7	.	.	PUNCT
ijassa-899	212	8	.	.	PUNCT
ijassa-899	213	1	.	.	PUNCT
ijassa-899	214	1	,	,	PUNCT
ijassa-899	214	2	yn	yn	PROPN
ijassa-899	214	3	)	)	PUNCT
ijassa-899	214	4	.	.	PUNCT
ijassa-899	215	1	vector	vector	NOUN
ijassa-899	215	2	xi	xi	PROPN
ijassa-899	215	3	has	have	VERB
ijassa-899	215	4	the	the	DET
ijassa-899	215	5	normal	normal	ADJ
ijassa-899	215	6	distribution	distribution	NOUN
ijassa-899	215	7	n	n	CCONJ
ijassa-899	215	8	(	(	PUNCT
ijassa-899	215	9	µj	µj	PROPN
ijassa-899	215	10	,	,	PUNCT
ijassa-899	215	11	σj	σj	ADJ
ijassa-899	215	12	/	/	SYM
ijassa-899	215	13	y	y	NOUN
ijassa-899	215	14	)	)	PUNCT
ijassa-899	215	15	conditioned	condition	VERB
ijassa-899	215	16	on	on	ADP
ijassa-899	215	17	tij	tij	PROPN
ijassa-899	215	18	=	=	SYM
ijassa-899	215	19	1	1	NUM
ijassa-899	215	20	and	and	CCONJ
ijassa-899	215	21	yi	yi	NOUN
ijassa-899	216	1	=	=	PUNCT
ijassa-899	216	2	y.	y.	NOUN
ijassa-899	216	3	the	the	DET
ijassa-899	216	4	joint	joint	ADJ
ijassa-899	216	5	distribution	distribution	NOUN
ijassa-899	216	6	of	of	ADP
ijassa-899	216	7	vectors	vector	NOUN
ijassa-899	216	8	(	(	PUNCT
ijassa-899	216	9	x	x	X
ijassa-899	216	10	,	,	PUNCT
ijassa-899	216	11	t	t	PROPN
ijassa-899	216	12	,	,	PUNCT
ijassa-899	216	13	y	y	PROPN
ijassa-899	216	14	)	)	PUNCT
ijassa-899	216	15	has	have	VERB
ijassa-899	216	16	the	the	DET
ijassa-899	216	17	density	density	NOUN
ijassa-899	216	18	(	(	PUNCT
ijassa-899	216	19	the	the	DET
ijassa-899	216	20	component	component	NOUN
ijassa-899	216	21	t	t	PROPN
ijassa-899	216	22	has	have	AUX
ijassa-899	216	23	discrete	discrete	ADJ
ijassa-899	216	24	density	density	NOUN
ijassa-899	216	25	)	)	PUNCT
ijassa-899	216	26	p(x	p(x	PROPN
ijassa-899	216	27	,	,	PUNCT
ijassa-899	216	28	t	t	PROPN
ijassa-899	216	29	,	,	PUNCT
ijassa-899	216	30	y|w	y|w	X
ijassa-899	216	31	,	,	PUNCT
ijassa-899	216	32	µ,σ	µ,σ	INTJ
ijassa-899	216	33	,	,	PUNCT
ijassa-899	216	34	ν	ν	NOUN
ijassa-899	216	35	)	)	PUNCT
ijassa-899	216	36	=	=	SYM
ijassa-899	216	37	n∏	n∏	PROPN
ijassa-899	216	38	i=1	i=1	PROPN
ijassa-899	217	1	k∏	k∏	PROPN
ijassa-899	217	2	j=1	j=1	NOUN
ijassa-899	218	1	[	[	X
ijassa-899	218	2	wjq(xi|µj	wjq(xi|µj	PROPN
ijassa-899	218	3	,	,	PUNCT
ijassa-899	218	4	σj	σj	ADJ
ijassa-899	218	5	/	/	SYM
ijassa-899	218	6	yi)γ(yi|ν/2	yi)γ(yi|ν/2	ADJ
ijassa-899	218	7	,	,	PUNCT
ijassa-899	218	8	ν/2)]tij	ν/2)]tij	ADV
ijassa-899	218	9	.	.	PUNCT
ijassa-899	219	1	compute	compute	PROPN
ijassa-899	219	2	ln	ln	PROPN
ijassa-899	219	3	p(x	p(x	PROPN
ijassa-899	219	4	,	,	PUNCT
ijassa-899	219	5	t	t	PROPN
ijassa-899	219	6	,	,	PUNCT
ijassa-899	219	7	y|w	y|w	X
ijassa-899	219	8	,	,	PUNCT
ijassa-899	219	9	µ,σ	µ,σ	INTJ
ijassa-899	219	10	,	,	PUNCT
ijassa-899	219	11	ν	ν	NOUN
ijassa-899	219	12	)	)	PUNCT
ijassa-899	220	1	=	=	SYM
ijassa-899	220	2	n∑	n∑	PROPN
ijassa-899	220	3	i=1	i=1	PROPN
ijassa-899	221	1	k∑	k∑	PROPN
ijassa-899	222	1	j=1	j=1	PROPN
ijassa-899	222	2	tij	tij	PROPN
ijassa-899	223	1	[	[	X
ijassa-899	223	2	lnwj	lnwj	X
ijassa-899	223	3	+	+	X
ijassa-899	223	4	ln	ln	ADJ
ijassa-899	223	5	q(xi|µj	q(xi|µj	NOUN
ijassa-899	223	6	,	,	PUNCT
ijassa-899	223	7	σj	σj	ADJ
ijassa-899	223	8	/	/	SYM
ijassa-899	223	9	yi	yi	NOUN
ijassa-899	223	10	)	)	PUNCT
ijassa-899	223	11	+	+	CCONJ
ijassa-899	223	12	ln	ln	ADJ
ijassa-899	223	13	γ(yi|ν/2	γ(yi|ν/2	NOUN
ijassa-899	223	14	,	,	PUNCT
ijassa-899	223	15	ν/2	ν/2	NUM
ijassa-899	223	16	)	)	PUNCT
ijassa-899	223	17	]	]	PUNCT
ijassa-899	224	1	=	=	PUNCT
ijassa-899	224	2	=	=	PUNCT
ijassa-899	224	3	n∑	n∑	PROPN
ijassa-899	224	4	i=1	i=1	PROPN
ijassa-899	224	5	k∑	k∑	PROPN
ijassa-899	225	1	j=1	j=1	PROPN
ijassa-899	225	2	tij	tij	PROPN
ijassa-899	226	1	[	[	PUNCT
ijassa-899	226	2	lnwj	lnwj	NOUN
ijassa-899	226	3	−	−	PROPN
ijassa-899	227	1	d	d	PROPN
ijassa-899	227	2	2	2	NUM
ijassa-899	227	3	ln	ln	NOUN
ijassa-899	227	4	2π	2π	NOUN
ijassa-899	227	5	−	−	NOUN
ijassa-899	227	6	1	1	NUM
ijassa-899	227	7	2	2	NUM
ijassa-899	227	8	ln	ln	NOUN
ijassa-899	227	9	det	det	NOUN
ijassa-899	227	10	σj	σj	VERB
ijassa-899	227	11	+	+	CCONJ
ijassa-899	227	12	d	d	PROPN
ijassa-899	227	13	2	2	NUM
ijassa-899	227	14	ln	ln	NOUN
ijassa-899	227	15	yi	yi	NOUN
ijassa-899	228	1	−	−	PROPN
ijassa-899	229	1	yi	yi	NOUN
ijassa-899	229	2	2	2	NUM
ijassa-899	229	3	(	(	PUNCT
ijassa-899	229	4	xij	xij	NOUN
ijassa-899	229	5	−	−	PROPN
ijassa-899	229	6	µj)tς−1	µj)tς−1	PROPN
ijassa-899	229	7	j	j	PROPN
ijassa-899	229	8	(	(	PUNCT
ijassa-899	229	9	xij	xij	NOUN
ijassa-899	229	10	−	−	PROPN
ijassa-899	229	11	µj)+	µj)+	NOUN
ijassa-899	229	12	+	+	CCONJ
ijassa-899	229	13	ν	ν	X
ijassa-899	229	14	2	2	NUM
ijassa-899	229	15	ln	ln	NOUN
ijassa-899	229	16	ν	ν	NOUN
ijassa-899	229	17	2	2	NUM
ijassa-899	229	18	−	−	PROPN
ijassa-899	229	19	ln	ln	ADJ
ijassa-899	229	20	γ(ν/2	γ(ν/2	NOUN
ijassa-899	229	21	)	)	PUNCT
ijassa-899	230	1	+	+	CCONJ
ijassa-899	230	2	(	(	PUNCT
ijassa-899	230	3	ν	ν	NOUN
ijassa-899	230	4	2	2	NUM
ijassa-899	230	5	−	−	NOUN
ijassa-899	230	6	1	1	NUM
ijassa-899	230	7	)	)	PUNCT
ijassa-899	230	8	ln	ln	ADJ
ijassa-899	230	9	yi	yi	NOUN
ijassa-899	230	10	−	−	NOUN
ijassa-899	230	11	ν	ν	PROPN
ijassa-899	230	12	2	2	NUM
ijassa-899	230	13	yi	yi	NOUN
ijassa-899	230	14	]	]	PUNCT
ijassa-899	230	15	.	.	PUNCT
ijassa-899	231	1	the	the	DET
ijassa-899	231	2	estimation	estimation	NOUN
ijassa-899	231	3	of	of	ADP
ijassa-899	231	4	the	the	DET
ijassa-899	231	5	mixture	mixture	NOUN
ijassa-899	231	6	parameters	parameter	NOUN
ijassa-899	231	7	is	be	AUX
ijassa-899	231	8	performed	perform	VERB
ijassa-899	231	9	by	by	ADP
ijassa-899	231	10	solving	solve	VERB
ijassa-899	231	11	the	the	DET
ijassa-899	231	12	problem	problem	NOUN
ijassa-899	231	13	of	of	ADP
ijassa-899	231	14	maximizing	maximize	VERB
ijassa-899	231	15	the	the	DET
ijassa-899	231	16	model	model	NOUN
ijassa-899	231	17	likelihood	likelihood	NOUN
ijassa-899	231	18	function	function	NOUN
ijassa-899	231	19	using	use	VERB
ijassa-899	231	20	em	em	PRON
ijassa-899	231	21	algorithm	algorithm	NOUN
ijassa-899	231	22	.	.	PUNCT
ijassa-899	232	1	at	at	ADP
ijassa-899	232	2	the	the	DET
ijassa-899	232	3	e	e	NOUN
ijassa-899	232	4	-	-	NOUN
ijassa-899	232	5	step	step	VERB
ijassa-899	232	6	the	the	DET
ijassa-899	232	7	distributions	distribution	NOUN
ijassa-899	232	8	copyright	copyright	NOUN
ijassa-899	232	9	©	©	PROPN
ijassa-899	232	10	2020	2020	NUM
ijassa-899	232	11	assa	assa	NOUN
ijassa-899	232	12	.	.	PUNCT
ijassa-899	233	1	adv	adv	PROPN
ijassa-899	233	2	syst	syst	PROPN
ijassa-899	233	3	sci	sci	PROPN
ijassa-899	233	4	appl	appl	PROPN
ijassa-899	233	5	(	(	PUNCT
ijassa-899	233	6	2020	2020	NUM
ijassa-899	233	7	)	)	PUNCT
ijassa-899	233	8	student	student	NOUN
ijassa-899	233	9	mixture	mixture	NOUN
ijassa-899	233	10	and	and	CCONJ
ijassa-899	233	11	its	its	PRON
ijassa-899	233	12	machine	machine	NOUN
ijassa-899	233	13	learning	learn	VERB
ijassa-899	233	14	applications	application	NOUN
ijassa-899	233	15	to	to	ADP
ijassa-899	233	16	pvt	pvt	PROPN
ijassa-899	233	17	properties	property	NOUN
ijassa-899	233	18	105	105	NUM
ijassa-899	233	19	of	of	ADP
ijassa-899	233	20	t	t	PROPN
ijassa-899	233	21	and	and	CCONJ
ijassa-899	233	22	y	y	PROPN
ijassa-899	233	23	are	be	AUX
ijassa-899	233	24	estimated	estimate	VERB
ijassa-899	233	25	iteratively	iteratively	ADV
ijassa-899	233	26	using	use	VERB
ijassa-899	233	27	variational	variational	ADJ
ijassa-899	233	28	bayesian	bayesian	NOUN
ijassa-899	233	29	inference	inference	NOUN
ijassa-899	233	30	.	.	PUNCT
ijassa-899	234	1	independence	independence	NOUN
ijassa-899	234	2	of	of	ADP
ijassa-899	234	3	posteriori	posteriori	NOUN
ijassa-899	234	4	distributions	distribution	NOUN
ijassa-899	234	5	(	(	PUNCT
ijassa-899	234	6	conditioned	condition	VERB
ijassa-899	234	7	on	on	ADP
ijassa-899	234	8	x	x	NOUN
ijassa-899	234	9	)	)	PUNCT
ijassa-899	234	10	is	be	AUX
ijassa-899	234	11	assumed	assume	VERB
ijassa-899	234	12	.	.	PUNCT
ijassa-899	235	1	these	these	DET
ijassa-899	235	2	results	result	NOUN
ijassa-899	235	3	are	be	AUX
ijassa-899	235	4	in	in	ADP
ijassa-899	235	5	the	the	DET
ijassa-899	235	6	following	follow	VERB
ijassa-899	235	7	iterative	iterative	NOUN
ijassa-899	235	8	scheme	scheme	NOUN
ijassa-899	235	9	.	.	PUNCT
ijassa-899	236	1	1	1	X
ijassa-899	236	2	.	.	X
ijassa-899	236	3	selection	selection	NOUN
ijassa-899	236	4	a	a	DET
ijassa-899	236	5	random	random	ADJ
ijassa-899	236	6	initial	initial	ADJ
ijassa-899	236	7	values	value	NOUN
ijassa-899	236	8	wj	wj	PROPN
ijassa-899	236	9	,	,	PUNCT
ijassa-899	236	10	µj	µj	PROPN
ijassa-899	236	11	,	,	PUNCT
ijassa-899	236	12	σj	σj	ADJ
ijassa-899	236	13	.	.	PUNCT
ijassa-899	237	1	vectors	vector	NOUN
ijassa-899	237	2	µj	µj	PROPN
ijassa-899	237	3	can	can	AUX
ijassa-899	237	4	be	be	AUX
ijassa-899	237	5	obtained	obtain	VERB
ijassa-899	237	6	from	from	ADP
ijassa-899	237	7	normal	normal	ADJ
ijassa-899	237	8	distribution	distribution	NOUN
ijassa-899	237	9	n	n	CCONJ
ijassa-899	237	10	(	(	PUNCT
ijassa-899	237	11	0	0	NUM
ijassa-899	237	12	,	,	PUNCT
ijassa-899	237	13	i	i	PROPN
ijassa-899	237	14	d	d	PROPN
ijassa-899	237	15	)	)	PUNCT
ijassa-899	237	16	,	,	PUNCT
ijassa-899	237	17	vectors	vector	NOUN
ijassa-899	237	18	wj	wj	X
ijassa-899	237	19	—	—	PUNCT
ijassa-899	237	20	from	from	ADP
ijassa-899	237	21	the	the	DET
ijassa-899	237	22	uniform	uniform	ADJ
ijassa-899	237	23	distribution	distribution	NOUN
ijassa-899	237	24	on	on	ADP
ijassa-899	237	25	the	the	DET
ijassa-899	237	26	simplex	simplex	NOUN
ijassa-899	237	27	(	(	PUNCT
ijassa-899	237	28	dirichlet	dirichlet	NOUN
ijassa-899	237	29	distribution	distribution	NOUN
ijassa-899	237	30	)	)	PUNCT
ijassa-899	237	31	.	.	PUNCT
ijassa-899	238	1	the	the	DET
ijassa-899	238	2	matrices	matrix	NOUN
ijassa-899	238	3	σj	σj	VERB
ijassa-899	238	4	can	can	AUX
ijassa-899	238	5	be	be	AUX
ijassa-899	238	6	generated	generate	VERB
ijassa-899	238	7	using	use	VERB
ijassa-899	238	8	the	the	DET
ijassa-899	238	9	wishart	wishart	NOUN
ijassa-899	238	10	distribution	distribution	NOUN
ijassa-899	239	1	[	[	X
ijassa-899	239	2	13	13	NUM
ijassa-899	239	3	]	]	SYM
ijassa-899	239	4	.	.	PUNCT
ijassa-899	240	1	2	2	X
ijassa-899	240	2	.	.	X
ijassa-899	240	3	e	e	X
ijassa-899	240	4	-	-	NOUN
ijassa-899	240	5	step	step	NOUN
ijassa-899	240	6	:	:	PUNCT
ijassa-899	240	7	(	(	PUNCT
ijassa-899	240	8	a	a	X
ijassa-899	240	9	)	)	PUNCT
ijassa-899	240	10	select	select	VERB
ijassa-899	240	11	a	a	DET
ijassa-899	240	12	random	random	ADJ
ijassa-899	240	13	initial	initial	ADJ
ijassa-899	240	14	value	value	NOUN
ijassa-899	240	15	for	for	ADP
ijassa-899	240	16	y	y	PROPN
ijassa-899	240	17	.	.	PUNCT
ijassa-899	241	1	(	(	PUNCT
ijassa-899	241	2	b	b	X
ijassa-899	241	3	)	)	PUNCT
ijassa-899	241	4	having	have	VERB
ijassa-899	241	5	the	the	DET
ijassa-899	241	6	current	current	ADJ
ijassa-899	241	7	distribution	distribution	NOUN
ijassa-899	241	8	of	of	ADP
ijassa-899	241	9	y	y	PROPN
ijassa-899	241	10	,	,	PUNCT
ijassa-899	241	11	approximate	approximate	VERB
ijassa-899	241	12	the	the	DET
ijassa-899	241	13	distribution	distribution	NOUN
ijassa-899	241	14	of	of	ADP
ijassa-899	241	15	t	t	PROPN
ijassa-899	241	16	.	.	PUNCT
ijassa-899	242	1	(	(	PUNCT
ijassa-899	242	2	c	c	X
ijassa-899	242	3	)	)	PUNCT
ijassa-899	242	4	having	have	VERB
ijassa-899	242	5	the	the	DET
ijassa-899	242	6	current	current	ADJ
ijassa-899	242	7	distribution	distribution	NOUN
ijassa-899	242	8	of	of	ADP
ijassa-899	242	9	t	t	PROPN
ijassa-899	242	10	,	,	PUNCT
ijassa-899	242	11	approximate	approximate	VERB
ijassa-899	242	12	the	the	DET
ijassa-899	242	13	distribution	distribution	NOUN
ijassa-899	242	14	of	of	ADP
ijassa-899	242	15	y	y	PROPN
ijassa-899	242	16	.	.	PUNCT
ijassa-899	243	1	(	(	PUNCT
ijassa-899	243	2	d	d	X
ijassa-899	243	3	)	)	PUNCT
ijassa-899	243	4	repeat	repeat	NOUN
ijassa-899	243	5	steps	step	NOUN
ijassa-899	243	6	b	b	NOUN
ijassa-899	243	7	-	-	PUNCT
ijassa-899	243	8	c	c	NOUN
ijassa-899	243	9	until	until	ADP
ijassa-899	243	10	convergence	convergence	NOUN
ijassa-899	243	11	of	of	ADP
ijassa-899	243	12	l.	l.	PROPN
ijassa-899	243	13	3	3	NUM
ijassa-899	243	14	.	.	PUNCT
ijassa-899	244	1	m	m	NOUN
ijassa-899	244	2	-	-	NOUN
ijassa-899	244	3	step	step	NOUN
ijassa-899	244	4	.	.	PUNCT
ijassa-899	245	1	4	4	X
ijassa-899	245	2	.	.	X
ijassa-899	245	3	repeat	repeat	NOUN
ijassa-899	245	4	e	e	PROPN
ijassa-899	245	5	and	and	CCONJ
ijassa-899	245	6	m	m	NOUN
ijassa-899	245	7	steps	step	NOUN
ijassa-899	245	8	until	until	ADP
ijassa-899	245	9	convergence	convergence	NOUN
ijassa-899	245	10	of	of	ADP
ijassa-899	245	11	l.	l.	PROPN
ijassa-899	245	12	4.1	4.1	NUM
ijassa-899	245	13	.	.	PUNCT
ijassa-899	246	1	e	e	X
ijassa-899	246	2	-	-	NOUN
ijassa-899	246	3	step	step	NOUN
ijassa-899	246	4	,	,	PUNCT
ijassa-899	246	5	internal	internal	ADJ
ijassa-899	246	6	step	step	NOUN
ijassa-899	246	7	i	i	PRON
ijassa-899	246	8	at	at	ADP
ijassa-899	246	9	this	this	DET
ijassa-899	246	10	step	step	NOUN
ijassa-899	246	11	,	,	PUNCT
ijassa-899	246	12	the	the	DET
ijassa-899	246	13	distribution	distribution	NOUN
ijassa-899	246	14	of	of	ADP
ijassa-899	246	15	t	t	NOUN
ijassa-899	246	16	conditioned	condition	VERB
ijassa-899	246	17	on	on	ADP
ijassa-899	246	18	x	x	SYM
ijassa-899	246	19	is	be	AUX
ijassa-899	246	20	approximated	approximate	VERB
ijassa-899	246	21	using	use	VERB
ijassa-899	246	22	the	the	DET
ijassa-899	246	23	relation	relation	NOUN
ijassa-899	246	24	ln	ln	NOUN
ijassa-899	246	25	r(t	r(t	NOUN
ijassa-899	246	26	)	)	PUNCT
ijassa-899	246	27	∝	∝	PROPN
ijassa-899	246	28	eγ	eγ	ADP
ijassa-899	246	29	ln	ln	PROPN
ijassa-899	246	30	p(x	p(x	PROPN
ijassa-899	246	31	,	,	PUNCT
ijassa-899	246	32	t	t	PROPN
ijassa-899	246	33	,	,	PUNCT
ijassa-899	246	34	y	y	PROPN
ijassa-899	246	35	|w	|w	PROPN
ijassa-899	246	36	,	,	PUNCT
ijassa-899	246	37	µ,σ	µ,σ	PROPN
ijassa-899	246	38	,	,	PUNCT
ijassa-899	246	39	ν	ν	NOUN
ijassa-899	246	40	)	)	PUNCT
ijassa-899	246	41	,	,	PUNCT
ijassa-899	246	42	where	where	SCONJ
ijassa-899	246	43	mathematical	mathematical	ADJ
ijassa-899	246	44	expectation	expectation	NOUN
ijassa-899	246	45	eγ	eγ	ADP
ijassa-899	246	46	is	be	AUX
ijassa-899	246	47	computed	compute	VERB
ijassa-899	246	48	under	under	ADP
ijassa-899	246	49	the	the	DET
ijassa-899	246	50	condition	condition	NOUN
ijassa-899	246	51	that	that	SCONJ
ijassa-899	246	52	the	the	DET
ijassa-899	246	53	current	current	ADJ
ijassa-899	246	54	distribution	distribution	NOUN
ijassa-899	246	55	of	of	ADP
ijassa-899	246	56	y	y	PROPN
ijassa-899	246	57	was	be	AUX
ijassa-899	246	58	computed	compute	VERB
ijassa-899	246	59	on	on	ADP
ijassa-899	246	60	the	the	DET
ijassa-899	246	61	previous	previous	ADJ
ijassa-899	246	62	iteration	iteration	NOUN
ijassa-899	246	63	of	of	ADP
ijassa-899	246	64	step	step	PROPN
ijassa-899	246	65	ii	ii	PROPN
ijassa-899	246	66	.	.	PROPN
ijassa-899	247	1	remark	remark	PROPN
ijassa-899	247	2	.	.	PUNCT
ijassa-899	248	1	everywhere	everywhere	ADV
ijassa-899	248	2	below	below	ADP
ijassa-899	248	3	symbol	symbol	NOUN
ijassa-899	248	4	∝	∝	PROPN
ijassa-899	248	5	means	mean	VERB
ijassa-899	248	6	equality	equality	NOUN
ijassa-899	248	7	up	up	ADP
ijassa-899	248	8	to	to	ADP
ijassa-899	248	9	a	a	DET
ijassa-899	248	10	multiplicative	multiplicative	ADJ
ijassa-899	248	11	constant	constant	NOUN
ijassa-899	248	12	for	for	ADP
ijassa-899	248	13	probabilities	probability	NOUN
ijassa-899	248	14	and	and	CCONJ
ijassa-899	248	15	equality	equality	NOUN
ijassa-899	248	16	up	up	ADP
ijassa-899	248	17	to	to	ADP
ijassa-899	248	18	an	an	DET
ijassa-899	248	19	additive	additive	ADJ
ijassa-899	248	20	constant	constant	NOUN
ijassa-899	248	21	for	for	ADP
ijassa-899	248	22	logarithms	logarithm	NOUN
ijassa-899	248	23	of	of	ADP
ijassa-899	248	24	probabilities	probability	NOUN
ijassa-899	248	25	.	.	PUNCT
ijassa-899	249	1	each	each	DET
ijassa-899	249	2	ti	ti	PROPN
ijassa-899	249	3	has	have	VERB
ijassa-899	249	4	a	a	DET
ijassa-899	249	5	discrete	discrete	ADJ
ijassa-899	249	6	distribution	distribution	NOUN
ijassa-899	249	7	with	with	ADP
ijassa-899	249	8	values	value	NOUN
ijassa-899	249	9	in	in	ADP
ijassa-899	249	10	a	a	DET
ijassa-899	249	11	set	set	NOUN
ijassa-899	249	12	of	of	ADP
ijassa-899	249	13	binary	binary	ADJ
ijassa-899	249	14	vectors	vector	NOUN
ijassa-899	249	15	that	that	PRON
ijassa-899	249	16	have	have	VERB
ijassa-899	249	17	exactly	exactly	ADV
ijassa-899	249	18	one	one	NUM
ijassa-899	249	19	unit	unit	NOUN
ijassa-899	249	20	.	.	PUNCT
ijassa-899	250	1	for	for	ADP
ijassa-899	250	2	this	this	DET
ijassa-899	250	3	distribution	distribution	NOUN
ijassa-899	250	4	,	,	PUNCT
ijassa-899	250	5	density	density	NOUN
ijassa-899	250	6	logarithm	logarithm	NOUN
ijassa-899	250	7	is	be	AUX
ijassa-899	250	8	∑k	∑k	PROPN
ijassa-899	250	9	j=1	j=1	PROPN
ijassa-899	250	10	tij	tij	PROPN
ijassa-899	250	11	ln	ln	PROPN
ijassa-899	250	12	rij	rij	PROPN
ijassa-899	250	13	,	,	PUNCT
ijassa-899	250	14	where	where	SCONJ
ijassa-899	250	15	rij	rij	ADJ
ijassa-899	250	16	=	=	PUNCT
ijassa-899	250	17	p(tij	p(tij	NOUN
ijassa-899	250	18	=	=	NOUN
ijassa-899	250	19	1	1	X
ijassa-899	250	20	)	)	PUNCT
ijassa-899	250	21	and	and	CCONJ
ijassa-899	250	22	∑k	∑k	PROPN
ijassa-899	250	23	j=1	j=1	ADJ
ijassa-899	250	24	rij	rij	X
ijassa-899	250	25	=	=	SYM
ijassa-899	250	26	1	1	X
ijassa-899	250	27	.	.	PUNCT
ijassa-899	250	28	ln	ln	ADJ
ijassa-899	250	29	r(t	r(t	NOUN
ijassa-899	250	30	)	)	PUNCT
ijassa-899	250	31	∝	∝	PROPN
ijassa-899	250	32	eγ	eγ	ADP
ijassa-899	250	33	ln	ln	PROPN
ijassa-899	250	34	p(x	p(x	PROPN
ijassa-899	250	35	,	,	PUNCT
ijassa-899	250	36	t	t	PROPN
ijassa-899	250	37	,	,	PUNCT
ijassa-899	250	38	y	y	PROPN
ijassa-899	250	39	|w	|w	PROPN
ijassa-899	250	40	,	,	PUNCT
ijassa-899	250	41	µ,σ	µ,σ	PROPN
ijassa-899	250	42	,	,	PUNCT
ijassa-899	250	43	ν	ν	NOUN
ijassa-899	250	44	)	)	PUNCT
ijassa-899	250	45	∝	∝	PROPN
ijassa-899	250	46	∝	∝	PROPN
ijassa-899	250	47	n∑	n∑	PROPN
ijassa-899	250	48	i=1	i=1	PROPN
ijassa-899	251	1	k∑	k∑	PROPN
ijassa-899	252	1	j=1	j=1	PROPN
ijassa-899	252	2	tij	tij	PROPN
ijassa-899	252	3	[	[	PUNCT
ijassa-899	252	4	lnwj	lnwj	NOUN
ijassa-899	252	5	−	−	PROPN
ijassa-899	252	6	1	1	NUM
ijassa-899	252	7	2	2	NUM
ijassa-899	252	8	ln	ln	NOUN
ijassa-899	252	9	det	det	NOUN
ijassa-899	252	10	σj	σj	VERB
ijassa-899	252	11	−	−	PROPN
ijassa-899	252	12	eγyi	eγyi	NOUN
ijassa-899	252	13	2	2	NUM
ijassa-899	252	14	(	(	PUNCT
ijassa-899	252	15	xi	xi	X
ijassa-899	252	16	−	−	PROPN
ijassa-899	252	17	µj)tς−1	µj)tς−1	PROPN
ijassa-899	252	18	j	j	PROPN
ijassa-899	252	19	(	(	PUNCT
ijassa-899	252	20	xi	xi	PROPN
ijassa-899	252	21	−	−	PROPN
ijassa-899	252	22	µj	µj	PROPN
ijassa-899	252	23	)	)	PUNCT
ijassa-899	252	24	]	]	PUNCT
ijassa-899	253	1	∝	∝	PROPN
ijassa-899	253	2	∝	∝	PROPN
ijassa-899	253	3	n∑	n∑	PROPN
ijassa-899	253	4	i=1	i=1	PROPN
ijassa-899	253	5	k∑	k∑	PROPN
ijassa-899	254	1	j=1	j=1	PROPN
ijassa-899	254	2	tij	tij	PROPN
ijassa-899	254	3	[	[	PUNCT
ijassa-899	254	4	lnwj	lnwj	NOUN
ijassa-899	254	5	−	−	PROPN
ijassa-899	254	6	1	1	NUM
ijassa-899	254	7	2	2	NUM
ijassa-899	254	8	ln	ln	PROPN
ijassa-899	254	9	det	det	NOUN
ijassa-899	254	10	(	(	PUNCT
ijassa-899	254	11	σj	σj	ADJ
ijassa-899	254	12	/	/	SYM
ijassa-899	254	13	eγyi)−	eγyi)−	NOUN
ijassa-899	254	14	1	1	NUM
ijassa-899	254	15	2	2	NUM
ijassa-899	254	16	(	(	PUNCT
ijassa-899	254	17	xi	xi	ADP
ijassa-899	254	18	−	−	PROPN
ijassa-899	254	19	µj)t	µj)t	PRON
ijassa-899	254	20	(	(	PUNCT
ijassa-899	254	21	σj	σj	NOUN
ijassa-899	254	22	/	/	SYM
ijassa-899	254	23	eγyi	eγyi	NOUN
ijassa-899	254	24	)	)	PUNCT
ijassa-899	254	25	−1	−1	NOUN
ijassa-899	254	26	(	(	PUNCT
ijassa-899	254	27	xi	xi	PROPN
ijassa-899	254	28	−	−	PROPN
ijassa-899	254	29	µj	µj	PROPN
ijassa-899	254	30	)	)	PUNCT
ijassa-899	254	31	]	]	PUNCT
ijassa-899	255	1	∝	∝	PROPN
ijassa-899	255	2	∝	∝	PROPN
ijassa-899	255	3	n∑	n∑	PROPN
ijassa-899	256	1	i=1	i=1	PROPN
ijassa-899	257	1	k∑	k∑	PROPN
ijassa-899	258	1	j=1	j=1	PROPN
ijassa-899	258	2	tij	tij	PROPN
ijassa-899	259	1	[	[	X
ijassa-899	259	2	lnwj	lnwj	X
ijassa-899	259	3	+	+	CCONJ
ijassa-899	260	1	ln	ln	ADJ
ijassa-899	260	2	q	q	X
ijassa-899	260	3	(	(	PUNCT
ijassa-899	260	4	xi	xi	ADP
ijassa-899	260	5	|µj	|µj	PROPN
ijassa-899	260	6	,	,	PUNCT
ijassa-899	260	7	σj	σj	ADJ
ijassa-899	260	8	/	/	SYM
ijassa-899	260	9	eγyi	eγyi	NOUN
ijassa-899	260	10	)	)	PUNCT
ijassa-899	260	11	]	]	PUNCT
ijassa-899	260	12	.	.	PUNCT
ijassa-899	261	1	the	the	DET
ijassa-899	261	2	resulting	result	VERB
ijassa-899	261	3	expression	expression	NOUN
ijassa-899	261	4	implies	imply	VERB
ijassa-899	261	5	that	that	SCONJ
ijassa-899	261	6	the	the	DET
ijassa-899	261	7	optimal	optimal	ADJ
ijassa-899	261	8	approximation	approximation	NOUN
ijassa-899	261	9	of	of	ADP
ijassa-899	261	10	conditional	conditional	ADJ
ijassa-899	261	11	distribution	distribution	NOUN
ijassa-899	261	12	of	of	ADP
ijassa-899	261	13	t	t	PROPN
ijassa-899	261	14	(	(	PUNCT
ijassa-899	261	15	provided	provide	VERB
ijassa-899	261	16	x	x	X
ijassa-899	261	17	)	)	PUNCT
ijassa-899	261	18	is	be	AUX
ijassa-899	261	19	such	such	ADJ
ijassa-899	261	20	that	that	SCONJ
ijassa-899	261	21	the	the	DET
ijassa-899	261	22	values	value	NOUN
ijassa-899	261	23	t1	t1	VERB
ijassa-899	261	24	,	,	PUNCT
ijassa-899	261	25	.	.	PUNCT
ijassa-899	261	26	.	.	PUNCT
ijassa-899	262	1	.	.	PUNCT
ijassa-899	263	1	,	,	PUNCT
ijassa-899	263	2	tn	tn	PROPN
ijassa-899	263	3	are	be	AUX
ijassa-899	263	4	independent	independent	ADJ
ijassa-899	263	5	and	and	CCONJ
ijassa-899	263	6	rij	rij	ADJ
ijassa-899	263	7	∝	∝	PROPN
ijassa-899	263	8	wjq	wjq	PROPN
ijassa-899	263	9	(	(	PUNCT
ijassa-899	263	10	xi	xi	ADP
ijassa-899	263	11	|µj	|µj	PROPN
ijassa-899	263	12	,	,	PUNCT
ijassa-899	263	13	σj	σj	ADJ
ijassa-899	263	14	/	/	SYM
ijassa-899	263	15	eγyi	eγyi	NOUN
ijassa-899	263	16	)	)	PUNCT
ijassa-899	263	17	.	.	PUNCT
ijassa-899	264	1	from	from	ADP
ijassa-899	264	2	the	the	DET
ijassa-899	264	3	condition	condition	NOUN
ijassa-899	265	1	∑k	∑k	PROPN
ijassa-899	265	2	j=1	j=1	ADJ
ijassa-899	265	3	rij	rij	X
ijassa-899	265	4	=	=	SYM
ijassa-899	265	5	1	1	NUM
ijassa-899	265	6	we	we	PRON
ijassa-899	265	7	get	get	VERB
ijassa-899	265	8	rij	rij	ADJ
ijassa-899	265	9	=	=	PUNCT
ijassa-899	265	10	wjq	wjq	NOUN
ijassa-899	265	11	(	(	PUNCT
ijassa-899	265	12	xi	xi	ADP
ijassa-899	265	13	|µj	|µj	PROPN
ijassa-899	265	14	,	,	PUNCT
ijassa-899	265	15	σj	σj	ADJ
ijassa-899	265	16	/	/	SYM
ijassa-899	265	17	eγyi	eγyi	NOUN
ijassa-899	265	18	)	)	PUNCT
ijassa-899	265	19	k∑	k∑	PROPN
ijassa-899	266	1	s=1	s=1	X
ijassa-899	266	2	wsq	wsq	NOUN
ijassa-899	266	3	(	(	PUNCT
ijassa-899	266	4	xi	xi	X
ijassa-899	266	5	|µs	|µs	PROPN
ijassa-899	266	6	,	,	PUNCT
ijassa-899	266	7	σs	σs	PROPN
ijassa-899	266	8	/	/	SYM
ijassa-899	266	9	eγyi	eγyi	PROPN
ijassa-899	266	10	)	)	PUNCT
ijassa-899	266	11	.	.	PUNCT
ijassa-899	267	1	the	the	DET
ijassa-899	267	2	expectation	expectation	NOUN
ijassa-899	267	3	eγyi	eγyi	NOUN
ijassa-899	267	4	is	be	AUX
ijassa-899	267	5	taken	take	VERB
ijassa-899	267	6	of	of	ADP
ijassa-899	267	7	the	the	DET
ijassa-899	267	8	current	current	ADJ
ijassa-899	267	9	approximation	approximation	NOUN
ijassa-899	267	10	of	of	ADP
ijassa-899	267	11	yi	yi	PROPN
ijassa-899	267	12	.	.	PUNCT
ijassa-899	268	1	copyright	copyright	NOUN
ijassa-899	268	2	©	©	PROPN
ijassa-899	268	3	2020	2020	NUM
ijassa-899	268	4	assa	assa	NOUN
ijassa-899	268	5	.	.	PUNCT
ijassa-899	269	1	adv	adv	PROPN
ijassa-899	269	2	syst	syst	PROPN
ijassa-899	269	3	sci	sci	PROPN
ijassa-899	269	4	appl	appl	PROPN
ijassa-899	269	5	(	(	PUNCT
ijassa-899	269	6	2020	2020	NUM
ijassa-899	269	7	)	)	PUNCT
ijassa-899	269	8	106	106	NUM
ijassa-899	269	9	n.a	n.a	PROPN
ijassa-899	269	10	.	.	PROPN
ijassa-899	269	11	volkov	volkov	PROPN
ijassa-899	269	12	,	,	PUNCT
ijassa-899	269	13	e.yu	e.yu	PROPN
ijassa-899	269	14	.	.	PROPN
ijassa-899	269	15	dakhova	dakhova	PROPN
ijassa-899	269	16	,	,	PUNCT
ijassa-899	269	17	s.a	s.a	PROPN
ijassa-899	269	18	.	.	PROPN
ijassa-899	269	19	budennyy	budennyy	PROPN
ijassa-899	269	20	,	,	PUNCT
ijassa-899	269	21	a.m.	a.m.	PROPN
ijassa-899	269	22	andrianova	andrianova	VERB
ijassa-899	269	23	4.2	4.2	NUM
ijassa-899	269	24	.	.	PUNCT
ijassa-899	270	1	e	e	X
ijassa-899	270	2	-	-	NOUN
ijassa-899	270	3	step	step	NOUN
ijassa-899	270	4	,	,	PUNCT
ijassa-899	270	5	internal	internal	ADJ
ijassa-899	270	6	step	step	NOUN
ijassa-899	270	7	ii	ii	NOUN
ijassa-899	270	8	at	at	ADP
ijassa-899	270	9	this	this	DET
ijassa-899	270	10	step	step	NOUN
ijassa-899	270	11	,	,	PUNCT
ijassa-899	270	12	the	the	DET
ijassa-899	270	13	distribution	distribution	NOUN
ijassa-899	270	14	of	of	ADP
ijassa-899	270	15	y	y	PRON
ijassa-899	270	16	conditioned	condition	VERB
ijassa-899	270	17	on	on	ADP
ijassa-899	270	18	x	x	SYM
ijassa-899	270	19	is	be	AUX
ijassa-899	270	20	computed	compute	VERB
ijassa-899	270	21	:	:	PUNCT
ijassa-899	270	22	ln	ln	ADJ
ijassa-899	270	23	γ(y	γ(y	PROPN
ijassa-899	270	24	)	)	PUNCT
ijassa-899	270	25	∝	∝	PROPN
ijassa-899	270	26	er	er	INTJ
ijassa-899	270	27	ln	ln	PROPN
ijassa-899	270	28	p(x	p(x	PROPN
ijassa-899	270	29	,	,	PUNCT
ijassa-899	270	30	t	t	PROPN
ijassa-899	270	31	,	,	PUNCT
ijassa-899	270	32	y|w	y|w	X
ijassa-899	270	33	,	,	PUNCT
ijassa-899	270	34	µ,σ	µ,σ	INTJ
ijassa-899	270	35	,	,	PUNCT
ijassa-899	270	36	ν	ν	NOUN
ijassa-899	270	37	)	)	PUNCT
ijassa-899	270	38	,	,	PUNCT
ijassa-899	270	39	where	where	SCONJ
ijassa-899	270	40	the	the	DET
ijassa-899	270	41	current	current	ADJ
ijassa-899	270	42	distribution	distribution	NOUN
ijassa-899	270	43	of	of	ADP
ijassa-899	270	44	t	t	PROPN
ijassa-899	270	45	is	be	AUX
ijassa-899	270	46	assumed	assume	VERB
ijassa-899	270	47	.	.	PUNCT
ijassa-899	271	1	let	let	VERB
ijassa-899	271	2	us	we	PRON
ijassa-899	271	3	write	write	VERB
ijassa-899	271	4	this	this	DET
ijassa-899	271	5	expression	expression	NOUN
ijassa-899	271	6	up	up	ADP
ijassa-899	271	7	to	to	ADP
ijassa-899	271	8	a	a	DET
ijassa-899	271	9	constant	constant	NOUN
ijassa-899	271	10	that	that	PRON
ijassa-899	271	11	does	do	AUX
ijassa-899	271	12	not	not	PART
ijassa-899	271	13	depend	depend	VERB
ijassa-899	271	14	on	on	ADP
ijassa-899	271	15	y	y	PROPN
ijassa-899	271	16	,	,	PUNCT
ijassa-899	271	17	given	give	VERB
ijassa-899	271	18	that∑k	that∑k	ADP
ijassa-899	271	19	j=1	j=1	ADJ
ijassa-899	271	20	rij	rij	X
ijassa-899	271	21	=	=	SYM
ijassa-899	271	22	1	1	NUM
ijassa-899	271	23	ln	ln	NOUN
ijassa-899	271	24	γ(y	γ(y	PROPN
ijassa-899	271	25	)	)	PUNCT
ijassa-899	272	1	=	=	PUNCT
ijassa-899	272	2	er	er	INTJ
ijassa-899	272	3	ln	ln	ADJ
ijassa-899	272	4	p(x	p(x	PROPN
ijassa-899	272	5	,	,	PUNCT
ijassa-899	272	6	t	t	PROPN
ijassa-899	272	7	,	,	PUNCT
ijassa-899	272	8	y|w	y|w	X
ijassa-899	272	9	,	,	PUNCT
ijassa-899	272	10	µ,σ	µ,σ	INTJ
ijassa-899	272	11	,	,	PUNCT
ijassa-899	272	12	ν	ν	NOUN
ijassa-899	272	13	)	)	PUNCT
ijassa-899	272	14	∝	∝	PROPN
ijassa-899	273	1	=	=	PUNCT
ijassa-899	273	2	n∑	n∑	PROPN
ijassa-899	273	3	i=1	i=1	PROPN
ijassa-899	274	1	k∑	k∑	PROPN
ijassa-899	275	1	j=1	j=1	PROPN
ijassa-899	275	2	ertij	ertij	PROPN
ijassa-899	275	3	[	[	PUNCT
ijassa-899	275	4	d	d	PROPN
ijassa-899	275	5	2	2	NUM
ijassa-899	275	6	ln	ln	NOUN
ijassa-899	275	7	yi	yi	NOUN
ijassa-899	275	8	−	−	PROPN
ijassa-899	275	9	yi	yi	NOUN
ijassa-899	275	10	2	2	NUM
ijassa-899	275	11	(	(	PUNCT
ijassa-899	275	12	xi	xi	ADP
ijassa-899	275	13	−	−	PROPN
ijassa-899	275	14	µj)tσj(xi	µj)tσj(xi	VERB
ijassa-899	275	15	−	−	PROPN
ijassa-899	275	16	µj	µj	X
ijassa-899	275	17	)	)	PUNCT
ijassa-899	276	1	+	+	CCONJ
ijassa-899	276	2	(	(	PUNCT
ijassa-899	276	3	ν	ν	NOUN
ijassa-899	276	4	2	2	NUM
ijassa-899	276	5	−	−	NOUN
ijassa-899	276	6	1	1	NUM
ijassa-899	276	7	)	)	PUNCT
ijassa-899	276	8	ln	ln	ADJ
ijassa-899	276	9	yi	yi	NOUN
ijassa-899	276	10	−	−	NOUN
ijassa-899	276	11	ν	ν	NOUN
ijassa-899	276	12	2	2	NUM
ijassa-899	276	13	yi	yi	NOUN
ijassa-899	276	14	]	]	PUNCT
ijassa-899	277	1	∝	∝	PROPN
ijassa-899	277	2	∝	∝	PROPN
ijassa-899	277	3	n∑	n∑	PROPN
ijassa-899	277	4	i=1	i=1	PROPN
ijassa-899	277	5	k∑	k∑	PROPN
ijassa-899	278	1	j=1	j=1	PROPN
ijassa-899	278	2	rij	rij	PROPN
ijassa-899	279	1	[	[	X
ijassa-899	279	2	(	(	PUNCT
ijassa-899	279	3	ν	ν	X
ijassa-899	279	4	+	+	CCONJ
ijassa-899	279	5	d	d	SYM
ijassa-899	279	6	2	2	NUM
ijassa-899	279	7	−	−	NOUN
ijassa-899	279	8	1	1	NUM
ijassa-899	279	9	)	)	PUNCT
ijassa-899	279	10	ln	ln	NOUN
ijassa-899	279	11	yi	yi	NOUN
ijassa-899	279	12	−	−	PROPN
ijassa-899	280	1	(	(	PUNCT
ijassa-899	280	2	ν	ν	PROPN
ijassa-899	280	3	2	2	NUM
ijassa-899	280	4	+	+	CCONJ
ijassa-899	280	5	1	1	NUM
ijassa-899	280	6	2	2	NUM
ijassa-899	280	7	(	(	PUNCT
ijassa-899	280	8	xi	xi	X
ijassa-899	280	9	−	−	PROPN
ijassa-899	280	10	µj)tς−1	µj)tς−1	PROPN
ijassa-899	280	11	j	j	PROPN
ijassa-899	280	12	(	(	PUNCT
ijassa-899	280	13	xi	xi	PROPN
ijassa-899	280	14	−	−	PROPN
ijassa-899	280	15	µj	µj	PROPN
ijassa-899	280	16	)	)	PUNCT
ijassa-899	280	17	)	)	PUNCT
ijassa-899	281	1	yi	yi	NOUN
ijassa-899	281	2	]	]	PUNCT
ijassa-899	282	1	=	=	PUNCT
ijassa-899	282	2	∝	∝	PROPN
ijassa-899	282	3	n∑	n∑	NOUN
ijassa-899	282	4	i=1	i=1	X
ijassa-899	283	1	[	[	X
ijassa-899	283	2	(	(	PUNCT
ijassa-899	283	3	ν	ν	X
ijassa-899	283	4	+	+	CCONJ
ijassa-899	283	5	d	d	SYM
ijassa-899	283	6	2	2	NUM
ijassa-899	283	7	−	−	NOUN
ijassa-899	283	8	1	1	NUM
ijassa-899	283	9	)	)	PUNCT
ijassa-899	283	10	ln	ln	NOUN
ijassa-899	283	11	yi	yi	NOUN
ijassa-899	283	12	−	−	PROPN
ijassa-899	284	1	(	(	PUNCT
ijassa-899	284	2	ν	ν	PROPN
ijassa-899	284	3	2	2	NUM
ijassa-899	284	4	+	+	CCONJ
ijassa-899	284	5	1	1	NUM
ijassa-899	284	6	2	2	NUM
ijassa-899	284	7	k∑	k∑	NOUN
ijassa-899	284	8	j=1	j=1	PROPN
ijassa-899	284	9	rij(xi	rij(xi	VERB
ijassa-899	284	10	−	−	PROPN
ijassa-899	284	11	µj)tς−1	µj)tς−1	PROPN
ijassa-899	284	12	j	j	PROPN
ijassa-899	284	13	(	(	PUNCT
ijassa-899	284	14	xi	xi	PROPN
ijassa-899	284	15	−	−	PROPN
ijassa-899	284	16	µj	µj	PROPN
ijassa-899	284	17	)	)	PUNCT
ijassa-899	284	18	)	)	PUNCT
ijassa-899	284	19	yi	yi	PROPN
ijassa-899	284	20	]	]	PUNCT
ijassa-899	284	21	.	.	PUNCT
ijassa-899	285	1	thus	thus	ADV
ijassa-899	285	2	,	,	PUNCT
ijassa-899	285	3	we	we	PRON
ijassa-899	285	4	obtain	obtain	VERB
ijassa-899	285	5	the	the	DET
ijassa-899	285	6	gamma	gamma	NOUN
ijassa-899	285	7	distribution	distribution	NOUN
ijassa-899	285	8	with	with	ADP
ijassa-899	285	9	parameters	parameter	NOUN
ijassa-899	285	10	ai	ai	VERB
ijassa-899	285	11	=	=	PUNCT
ijassa-899	285	12	ν	ν	NOUN
ijassa-899	285	13	2	2	NUM
ijassa-899	285	14	+	+	CCONJ
ijassa-899	285	15	1	1	NUM
ijassa-899	285	16	2	2	NUM
ijassa-899	285	17	k∑	k∑	NOUN
ijassa-899	285	18	j=1	j=1	PROPN
ijassa-899	285	19	rij	rij	PROPN
ijassa-899	285	20	(	(	PUNCT
ijassa-899	285	21	xi	xi	X
ijassa-899	285	22	−	−	PROPN
ijassa-899	285	23	µj)tς−1	µj)tς−1	PROPN
ijassa-899	285	24	j	j	PROPN
ijassa-899	285	25	(	(	PUNCT
ijassa-899	285	26	xi	xi	PROPN
ijassa-899	285	27	−	−	PROPN
ijassa-899	285	28	µj	µj	PROPN
ijassa-899	285	29	)	)	PUNCT
ijassa-899	285	30	,	,	PUNCT
ijassa-899	285	31	bi	bi	NOUN
ijassa-899	285	32	=	=	NOUN
ijassa-899	285	33	ν	ν	PROPN
ijassa-899	285	34	+	+	CCONJ
ijassa-899	285	35	d	d	NOUN
ijassa-899	285	36	2	2	NUM
ijassa-899	285	37	.	.	PUNCT
ijassa-899	286	1	the	the	DET
ijassa-899	286	2	values	value	NOUN
ijassa-899	286	3	of	of	ADP
ijassa-899	286	4	mathematical	mathematical	ADJ
ijassa-899	286	5	expectations	expectation	NOUN
ijassa-899	286	6	are	be	AUX
ijassa-899	286	7	updated	update	VERB
ijassa-899	286	8	using	use	VERB
ijassa-899	286	9	relations	relation	NOUN
ijassa-899	286	10	eγyi	eγyi	NOUN
ijassa-899	286	11	=	=	PUNCT
ijassa-899	286	12	bi	bi	NOUN
ijassa-899	286	13	ai	ai	VERB
ijassa-899	286	14	and	and	CCONJ
ijassa-899	286	15	eγ	eγ	ADP
ijassa-899	286	16	lnyi	lnyi	NOUN
ijassa-899	286	17	=	=	SYM
ijassa-899	286	18	ψ(bi)−	ψ(bi)−	NOUN
ijassa-899	286	19	ln	ln	ADJ
ijassa-899	286	20	ai	ai	NOUN
ijassa-899	286	21	,	,	PUNCT
ijassa-899	286	22	which	which	PRON
ijassa-899	286	23	are	be	AUX
ijassa-899	286	24	stated	state	VERB
ijassa-899	286	25	in	in	ADP
ijassa-899	286	26	section	section	NOUN
ijassa-899	286	27	2	2	NUM
ijassa-899	286	28	.	.	PUNCT
ijassa-899	287	1	for	for	ADP
ijassa-899	287	2	brevity	brevity	NOUN
ijassa-899	287	3	,	,	PUNCT
ijassa-899	287	4	denote	denote	VERB
ijassa-899	287	5	ci	ci	NOUN
ijassa-899	287	6	=	=	SYM
ijassa-899	287	7	bi	bi	PROPN
ijassa-899	287	8	/	/	SYM
ijassa-899	287	9	ai	ai	NOUN
ijassa-899	287	10	.	.	PROPN
ijassa-899	288	1	4.3	4.3	NUM
ijassa-899	288	2	.	.	PUNCT
ijassa-899	289	1	m	m	NOUN
ijassa-899	289	2	-	-	NOUN
ijassa-899	289	3	step	step	NOUN
ijassa-899	289	4	at	at	ADP
ijassa-899	289	5	this	this	DET
ijassa-899	289	6	step	step	NOUN
ijassa-899	289	7	,	,	PUNCT
ijassa-899	289	8	the	the	DET
ijassa-899	289	9	values	value	NOUN
ijassa-899	289	10	of	of	ADP
ijassa-899	289	11	the	the	DET
ijassa-899	289	12	mixture	mixture	NOUN
ijassa-899	289	13	parameters	parameter	NOUN
ijassa-899	289	14	are	be	AUX
ijassa-899	289	15	updated	update	VERB
ijassa-899	289	16	by	by	ADP
ijassa-899	289	17	maximizing	maximize	VERB
ijassa-899	289	18	er	er	INTJ
ijassa-899	289	19	,	,	PUNCT
ijassa-899	289	20	γ	γ	PROPN
ijassa-899	289	21	ln	ln	ADJ
ijassa-899	289	22	p(x	p(x	PROPN
ijassa-899	289	23	,	,	PUNCT
ijassa-899	289	24	t	t	PROPN
ijassa-899	289	25	,	,	PUNCT
ijassa-899	289	26	y	y	PROPN
ijassa-899	289	27	|w	|w	PROPN
ijassa-899	289	28	,	,	PUNCT
ijassa-899	289	29	µ,σ	µ,σ	PROPN
ijassa-899	289	30	,	,	PUNCT
ijassa-899	289	31	ν	ν	NOUN
ijassa-899	289	32	)	)	PUNCT
ijassa-899	289	33	,	,	PUNCT
ijassa-899	289	34	where	where	SCONJ
ijassa-899	289	35	the	the	DET
ijassa-899	289	36	current	current	ADJ
ijassa-899	289	37	distributions	distribution	NOUN
ijassa-899	289	38	of	of	ADP
ijassa-899	289	39	t	t	PROPN
ijassa-899	289	40	and	and	CCONJ
ijassa-899	289	41	y	y	PROPN
ijassa-899	289	42	are	be	AUX
ijassa-899	289	43	assumed	assume	VERB
ijassa-899	289	44	.	.	PUNCT
ijassa-899	290	1	we	we	PRON
ijassa-899	290	2	leave	leave	VERB
ijassa-899	290	3	only	only	ADV
ijassa-899	290	4	those	those	DET
ijassa-899	290	5	summands	summand	NOUN
ijassa-899	290	6	that	that	PRON
ijassa-899	290	7	depend	depend	VERB
ijassa-899	290	8	on	on	ADP
ijassa-899	290	9	wj	wj	PROPN
ijassa-899	290	10	,	,	PUNCT
ijassa-899	290	11	µj	µj	PROPN
ijassa-899	290	12	,	,	PUNCT
ijassa-899	290	13	σj	σj	ADJ
ijassa-899	290	14	fx	fx	PROPN
ijassa-899	290	15	,	,	PUNCT
ijassa-899	290	16	ν(w	ν(w	NOUN
ijassa-899	290	17	,	,	PUNCT
ijassa-899	290	18	µ,σ	µ,σ	INTJ
ijassa-899	290	19	)	)	PUNCT
ijassa-899	290	20	=	=	SYM
ijassa-899	290	21	er	er	INTJ
ijassa-899	290	22	,	,	PUNCT
ijassa-899	290	23	γ	γ	PROPN
ijassa-899	290	24	ln	ln	ADJ
ijassa-899	290	25	p(x	p(x	PROPN
ijassa-899	290	26	,	,	PUNCT
ijassa-899	290	27	t	t	PROPN
ijassa-899	290	28	,	,	PUNCT
ijassa-899	290	29	y	y	PROPN
ijassa-899	290	30	|w	|w	PROPN
ijassa-899	290	31	,	,	PUNCT
ijassa-899	290	32	µ,σ	µ,σ	PROPN
ijassa-899	290	33	,	,	PUNCT
ijassa-899	290	34	ν	ν	NOUN
ijassa-899	290	35	)	)	PUNCT
ijassa-899	290	36	∝	∝	PROPN
ijassa-899	290	37	∝	∝	PROPN
ijassa-899	290	38	n∑	n∑	PROPN
ijassa-899	290	39	i=1	i=1	PROPN
ijassa-899	291	1	k∑	k∑	PROPN
ijassa-899	292	1	j=1	j=1	PROPN
ijassa-899	292	2	ertij	ertij	PROPN
ijassa-899	292	3	[	[	PUNCT
ijassa-899	292	4	lnwj	lnwj	NOUN
ijassa-899	292	5	−	−	PROPN
ijassa-899	292	6	1	1	NUM
ijassa-899	292	7	2	2	NUM
ijassa-899	292	8	ln	ln	NOUN
ijassa-899	292	9	det	det	NOUN
ijassa-899	292	10	σj	σj	VERB
ijassa-899	292	11	−	−	PROPN
ijassa-899	292	12	1	1	NUM
ijassa-899	292	13	2	2	NUM
ijassa-899	292	14	(	(	PUNCT
ijassa-899	292	15	xi	xi	X
ijassa-899	292	16	−	−	PROPN
ijassa-899	292	17	µj)tς−1	µj)tς−1	PROPN
ijassa-899	292	18	j	j	PROPN
ijassa-899	292	19	(	(	PUNCT
ijassa-899	292	20	xi	xi	INTJ
ijassa-899	292	21	−	−	PROPN
ijassa-899	292	22	µj)eγyi	µj)eγyi	ADV
ijassa-899	292	23	]	]	PUNCT
ijassa-899	293	1	=	=	PUNCT
ijassa-899	293	2	=	=	PUNCT
ijassa-899	293	3	n∑	n∑	PROPN
ijassa-899	293	4	i=1	i=1	PROPN
ijassa-899	293	5	k∑	k∑	PROPN
ijassa-899	293	6	j=1	j=1	PROPN
ijassa-899	293	7	rij	rij	X
ijassa-899	293	8	[	[	PUNCT
ijassa-899	293	9	lnwj	lnwj	NOUN
ijassa-899	293	10	−	−	PROPN
ijassa-899	293	11	1	1	NUM
ijassa-899	293	12	2	2	NUM
ijassa-899	293	13	ln	ln	NOUN
ijassa-899	293	14	det	det	NOUN
ijassa-899	293	15	σj	σj	VERB
ijassa-899	293	16	−	−	PROPN
ijassa-899	293	17	ci	ci	NOUN
ijassa-899	293	18	2	2	NUM
ijassa-899	293	19	(	(	PUNCT
ijassa-899	293	20	xi	xi	X
ijassa-899	293	21	−	−	PROPN
ijassa-899	293	22	µj)tς−1	µj)tς−1	PROPN
ijassa-899	293	23	j	j	PROPN
ijassa-899	293	24	(	(	PUNCT
ijassa-899	293	25	xi	xi	PROPN
ijassa-899	293	26	−	−	PROPN
ijassa-899	293	27	µj	µj	PROPN
ijassa-899	293	28	)	)	PUNCT
ijassa-899	293	29	]	]	PUNCT
ijassa-899	293	30	.	.	PUNCT
ijassa-899	294	1	maximization	maximization	NOUN
ijassa-899	294	2	by	by	ADP
ijassa-899	294	3	wj	wj	PROPN
ijassa-899	294	4	of	of	ADP
ijassa-899	294	5	fx	fx	PROPN
ijassa-899	294	6	,	,	PUNCT
ijassa-899	294	7	ν(w	ν(w	PROPN
ijassa-899	294	8	,	,	PUNCT
ijassa-899	294	9	µ,σ	µ,σ	NOUN
ijassa-899	294	10	)	)	PUNCT
ijassa-899	294	11	is	be	AUX
ijassa-899	294	12	equivalent	equivalent	ADJ
ijassa-899	294	13	to	to	ADP
ijassa-899	294	14	finding	find	VERB
ijassa-899	294	15	a	a	DET
ijassa-899	294	16	solution	solution	NOUN
ijassa-899	294	17	of	of	ADP
ijassa-899	294	18	the	the	DET
ijassa-899	294	19	problem	problem	PROPN
ijassa-899	294	20	n∑	n∑	PROPN
ijassa-899	295	1	i=1	i=1	PROPN
ijassa-899	296	1	k∑	k∑	PROPN
ijassa-899	297	1	j=1	j=1	PROPN
ijassa-899	297	2	rij	rij	PROPN
ijassa-899	297	3	lnwj	lnwj	PROPN
ijassa-899	297	4	−→	−→	PROPN
ijassa-899	297	5	max	max	PROPN
ijassa-899	297	6	w	w	PROPN
ijassa-899	297	7	,	,	PUNCT
ijassa-899	297	8	k∑	k∑	PROPN
ijassa-899	298	1	j=1	j=1	PROPN
ijassa-899	298	2	wj	wj	PROPN
ijassa-899	298	3	=	=	SYM
ijassa-899	298	4	1	1	PROPN
ijassa-899	298	5	,	,	PUNCT
ijassa-899	298	6	wj	wj	X
ijassa-899	298	7	>	>	X
ijassa-899	298	8	0	0	PROPN
ijassa-899	298	9	.	.	PUNCT
ijassa-899	299	1	copyright	copyright	NOUN
ijassa-899	299	2	©	©	PROPN
ijassa-899	299	3	2020	2020	NUM
ijassa-899	299	4	assa	assa	NOUN
ijassa-899	299	5	.	.	PUNCT
ijassa-899	300	1	adv	adv	PROPN
ijassa-899	300	2	syst	syst	PROPN
ijassa-899	300	3	sci	sci	PROPN
ijassa-899	300	4	appl	appl	PROPN
ijassa-899	300	5	(	(	PUNCT
ijassa-899	300	6	2020	2020	NUM
ijassa-899	300	7	)	)	PUNCT
ijassa-899	300	8	student	student	NOUN
ijassa-899	300	9	mixture	mixture	NOUN
ijassa-899	300	10	and	and	CCONJ
ijassa-899	300	11	its	its	PRON
ijassa-899	300	12	machine	machine	NOUN
ijassa-899	300	13	learning	learn	VERB
ijassa-899	300	14	applications	application	NOUN
ijassa-899	300	15	to	to	ADP
ijassa-899	300	16	pvt	pvt	PROPN
ijassa-899	300	17	properties	property	NOUN
ijassa-899	300	18	107	107	NUM
ijassa-899	300	19	let	let	VERB
ijassa-899	300	20	us	we	PRON
ijassa-899	300	21	forget	forget	VERB
ijassa-899	300	22	about	about	ADP
ijassa-899	300	23	the	the	DET
ijassa-899	300	24	restrictions	restriction	NOUN
ijassa-899	300	25	of	of	ADP
ijassa-899	300	26	the	the	DET
ijassa-899	300	27	inequality	inequality	NOUN
ijassa-899	300	28	type	type	NOUN
ijassa-899	300	29	for	for	ADP
ijassa-899	300	30	a	a	DET
ijassa-899	300	31	while	while	NOUN
ijassa-899	300	32	,	,	PUNCT
ijassa-899	300	33	make	make	VERB
ijassa-899	300	34	a	a	DET
ijassa-899	300	35	lagrange	lagrange	NOUN
ijassa-899	300	36	function	function	NOUN
ijassa-899	300	37	,	,	PUNCT
ijassa-899	300	38	and	and	CCONJ
ijassa-899	300	39	find	find	VERB
ijassa-899	300	40	its	its	PRON
ijassa-899	300	41	maximum	maximum	ADJ
ijassa-899	300	42	l	l	NOUN
ijassa-899	301	1	=	=	PUNCT
ijassa-899	301	2	n∑	n∑	PROPN
ijassa-899	301	3	i=1	i=1	PROPN
ijassa-899	302	1	k∑	k∑	PROPN
ijassa-899	303	1	j=1	j=1	PROPN
ijassa-899	303	2	rij	rij	PROPN
ijassa-899	303	3	lnwj	lnwj	PROPN
ijassa-899	303	4	−	−	PROPN
ijassa-899	303	5	λ	λ	PROPN
ijassa-899	303	6	(	(	PUNCT
ijassa-899	303	7	k∑	k∑	VERB
ijassa-899	303	8	j=1	j=1	NOUN
ijassa-899	303	9	wj	wj	PROPN
ijassa-899	303	10	−	−	PROPN
ijassa-899	303	11	1	1	NUM
ijassa-899	303	12	)	)	PUNCT
ijassa-899	303	13	,	,	PUNCT
ijassa-899	303	14	∂l	∂l	PROPN
ijassa-899	303	15	∂wj	∂wj	PROPN
ijassa-899	303	16	=	=	SYM
ijassa-899	303	17	1	1	NUM
ijassa-899	303	18	wj	wj	PROPN
ijassa-899	303	19	n∑	n∑	PROPN
ijassa-899	303	20	i=1	i=1	PROPN
ijassa-899	303	21	rij	rij	ADJ
ijassa-899	303	22	−	−	PROPN
ijassa-899	303	23	λ	λ	PROPN
ijassa-899	303	24	=	=	SYM
ijassa-899	303	25	0	0	NUM
ijassa-899	303	26	,	,	PUNCT
ijassa-899	303	27	wj	wj	NOUN
ijassa-899	303	28	=	=	SYM
ijassa-899	303	29	1	1	NUM
ijassa-899	303	30	λ	λ	PROPN
ijassa-899	303	31	n∑	n∑	NOUN
ijassa-899	303	32	i=1	i=1	PROPN
ijassa-899	303	33	rij	rij	PROPN
ijassa-899	303	34	.	.	PUNCT
ijassa-899	304	1	from	from	ADP
ijassa-899	304	2	the	the	DET
ijassa-899	304	3	condition	condition	NOUN
ijassa-899	305	1	∑k	∑k	PROPN
ijassa-899	305	2	j=1	j=1	NOUN
ijassa-899	305	3	wj	wj	PROPN
ijassa-899	305	4	=	=	PUNCT
ijassa-899	305	5	1	1	NUM
ijassa-899	305	6	we	we	PRON
ijassa-899	305	7	get	get	VERB
ijassa-899	305	8	wj	wj	PROPN
ijassa-899	306	1	=	=	PUNCT
ijassa-899	306	2	n∑	n∑	PROPN
ijassa-899	306	3	i=1	i=1	PROPN
ijassa-899	307	1	rij	rij	PROPN
ijassa-899	307	2	/	/	SYM
ijassa-899	308	1	n∑	n∑	NOUN
ijassa-899	308	2	i=1	i=1	PROPN
ijassa-899	309	1	k∑	k∑	PROPN
ijassa-899	309	2	s=1	s=1	X
ijassa-899	310	1	ris	ris	X
ijassa-899	310	2	.	.	PUNCT
ijassa-899	311	1	note	note	VERB
ijassa-899	311	2	that	that	SCONJ
ijassa-899	311	3	the	the	DET
ijassa-899	311	4	conditions	condition	NOUN
ijassa-899	311	5	wj	wj	X
ijassa-899	311	6	>	>	X
ijassa-899	311	7	0	0	NUM
ijassa-899	311	8	are	be	AUX
ijassa-899	311	9	met	meet	VERB
ijassa-899	311	10	.	.	PUNCT
ijassa-899	312	1	the	the	DET
ijassa-899	312	2	problem	problem	NOUN
ijassa-899	312	3	is	be	AUX
ijassa-899	312	4	solved	solve	VERB
ijassa-899	312	5	due	due	ADP
ijassa-899	312	6	to	to	ADP
ijassa-899	312	7	the	the	DET
ijassa-899	312	8	convexity	convexity	NOUN
ijassa-899	312	9	.	.	PUNCT
ijassa-899	313	1	to	to	PART
ijassa-899	313	2	maximize	maximize	VERB
ijassa-899	313	3	by	by	ADP
ijassa-899	313	4	µj	µj	PROPN
ijassa-899	313	5	,	,	PUNCT
ijassa-899	313	6	we	we	PRON
ijassa-899	313	7	equate	equate	VERB
ijassa-899	313	8	the	the	DET
ijassa-899	313	9	derivative	derivative	NOUN
ijassa-899	313	10	of	of	ADP
ijassa-899	313	11	fx	fx	PROPN
ijassa-899	313	12	,	,	PUNCT
ijassa-899	313	13	ν(w	ν(w	PROPN
ijassa-899	313	14	,	,	PUNCT
ijassa-899	313	15	µ,σ	µ,σ	NOUN
ijassa-899	313	16	)	)	PUNCT
ijassa-899	313	17	with	with	ADP
ijassa-899	313	18	respect	respect	NOUN
ijassa-899	313	19	to	to	ADP
ijassa-899	313	20	vector	vector	NOUN
ijassa-899	313	21	µj	µj	ADP
ijassa-899	313	22	to	to	ADP
ijassa-899	313	23	zero	zero	NUM
ijassa-899	313	24	∂fx	∂fx	NOUN
ijassa-899	313	25	,	,	PUNCT
ijassa-899	313	26	ν(w	ν(w	PROPN
ijassa-899	313	27	,	,	PUNCT
ijassa-899	313	28	µ,σ	µ,σ	INTJ
ijassa-899	313	29	)	)	PUNCT
ijassa-899	313	30	∂µj	∂µj	PROPN
ijassa-899	313	31	=	=	SYM
ijassa-899	314	1	n∑	n∑	PROPN
ijassa-899	314	2	i=1	i=1	PROPN
ijassa-899	314	3	rijci	rijci	NOUN
ijassa-899	315	1	σ−1	σ−1	INTJ
ijassa-899	315	2	j	j	PROPN
ijassa-899	315	3	(	(	PUNCT
ijassa-899	315	4	xi	xi	PROPN
ijassa-899	315	5	−	−	PROPN
ijassa-899	315	6	µj	µj	PROPN
ijassa-899	315	7	)	)	PUNCT
ijassa-899	315	8	=	=	SYM
ijassa-899	315	9	0	0	NUM
ijassa-899	315	10	,	,	PUNCT
ijassa-899	315	11	n∑	n∑	NOUN
ijassa-899	315	12	i=1	i=1	PROPN
ijassa-899	315	13	rijcixi	rijcixi	PROPN
ijassa-899	315	14	=	=	PUNCT
ijassa-899	315	15	n∑	n∑	PROPN
ijassa-899	315	16	i=1	i=1	PROPN
ijassa-899	315	17	rijciµj	rijciµj	NOUN
ijassa-899	315	18	,	,	PUNCT
ijassa-899	315	19	µj	µj	PROPN
ijassa-899	316	1	=	=	SYM
ijassa-899	316	2	n∑	n∑	PROPN
ijassa-899	316	3	i=1	i=1	PROPN
ijassa-899	317	1	rijci	rijci	PROPN
ijassa-899	317	2	xi	xi	PROPN
ijassa-899	317	3	/	/	SYM
ijassa-899	318	1	n∑	n∑	PROPN
ijassa-899	318	2	i=1	i=1	PROPN
ijassa-899	318	3	rijci	rijci	PROPN
ijassa-899	318	4	.	.	PUNCT
ijassa-899	319	1	to	to	PART
ijassa-899	319	2	maximize	maximize	VERB
ijassa-899	319	3	by	by	ADP
ijassa-899	319	4	σj	σj	NOUN
ijassa-899	319	5	,	,	PUNCT
ijassa-899	319	6	we	we	PRON
ijassa-899	319	7	equate	equate	VERB
ijassa-899	319	8	the	the	DET
ijassa-899	319	9	derivative	derivative	NOUN
ijassa-899	319	10	of	of	ADP
ijassa-899	319	11	fx	fx	PROPN
ijassa-899	319	12	,	,	PUNCT
ijassa-899	319	13	ν(w	ν(w	PROPN
ijassa-899	319	14	,	,	PUNCT
ijassa-899	319	15	µ,σ	µ,σ	NOUN
ijassa-899	319	16	)	)	PUNCT
ijassa-899	319	17	with	with	ADP
ijassa-899	319	18	respect	respect	NOUN
ijassa-899	319	19	to	to	PART
ijassa-899	319	20	matrix	matrix	NOUN
ijassa-899	319	21	σj	σj	VERB
ijassa-899	319	22	to	to	ADP
ijassa-899	319	23	zero	zero	NUM
ijassa-899	319	24	.	.	PUNCT
ijassa-899	320	1	note	note	VERB
ijassa-899	320	2	that	that	SCONJ
ijassa-899	320	3	(	(	PUNCT
ijassa-899	320	4	xi	xi	X
ijassa-899	320	5	−	−	PROPN
ijassa-899	320	6	µj)tς−1	µj)tς−1	PROPN
ijassa-899	320	7	j	j	PROPN
ijassa-899	320	8	(	(	PUNCT
ijassa-899	320	9	xi	xi	PROPN
ijassa-899	320	10	−	−	PROPN
ijassa-899	320	11	µj	µj	PROPN
ijassa-899	320	12	)	)	PUNCT
ijassa-899	320	13	=	=	PUNCT
ijassa-899	320	14	tr	tr	VERB
ijassa-899	320	15	(	(	PUNCT
ijassa-899	320	16	(	(	PUNCT
ijassa-899	320	17	xi	xi	INTJ
ijassa-899	320	18	−	−	PROPN
ijassa-899	320	19	µj)tς−1	µj)tς−1	PROPN
ijassa-899	320	20	j	j	PROPN
ijassa-899	320	21	(	(	PUNCT
ijassa-899	320	22	xi	xi	PROPN
ijassa-899	320	23	−	−	PROPN
ijassa-899	320	24	µj	µj	PROPN
ijassa-899	320	25	)	)	PUNCT
ijassa-899	320	26	)	)	PUNCT
ijassa-899	321	1	=	=	PUNCT
ijassa-899	321	2	tr	tr	PRON
ijassa-899	321	3	(	(	PUNCT
ijassa-899	321	4	σ−1	σ−1	PROPN
ijassa-899	321	5	j	j	PROPN
ijassa-899	321	6	(	(	PUNCT
ijassa-899	321	7	xi	xi	X
ijassa-899	321	8	−	−	PROPN
ijassa-899	321	9	µj)(xi	µj)(xi	PUNCT
ijassa-899	321	10	−	−	PROPN
ijassa-899	321	11	µj)t	µj)t	NUM
ijassa-899	321	12	)	)	PUNCT
ijassa-899	321	13	.	.	PUNCT
ijassa-899	322	1	using	use	VERB
ijassa-899	322	2	this	this	DET
ijassa-899	322	3	transformation	transformation	NOUN
ijassa-899	322	4	,	,	PUNCT
ijassa-899	322	5	as	as	ADV
ijassa-899	322	6	well	well	ADV
ijassa-899	322	7	as	as	ADP
ijassa-899	322	8	matrix	matrix	VERB
ijassa-899	322	9	derivative	derivative	ADJ
ijassa-899	322	10	formulas	formula	NOUN
ijassa-899	322	11	for	for	ADP
ijassa-899	322	12	square	square	ADJ
ijassa-899	322	13	matrices	matrix	NOUN
ijassa-899	322	14	[	[	X
ijassa-899	322	15	14	14	NUM
ijassa-899	322	16	]	]	SYM
ijassa-899	322	17	∂	∂	NUM
ijassa-899	322	18	∂x	∂x	PROPN
ijassa-899	322	19	detx	detx	NOUN
ijassa-899	322	20	=	=	SYM
ijassa-899	322	21	detx	detx	NOUN
ijassa-899	322	22	·	·	PUNCT
ijassa-899	322	23	x−t	x−t	PROPN
ijassa-899	322	24	,	,	PUNCT
ijassa-899	322	25	∂	∂	NUM
ijassa-899	322	26	∂x	∂x	PROPN
ijassa-899	322	27	tr	tr	PUNCT
ijassa-899	322	28	(	(	PUNCT
ijassa-899	322	29	x−1a	x−1a	PROPN
ijassa-899	322	30	)	)	PUNCT
ijassa-899	323	1	=	=	SYM
ijassa-899	324	1	−	−	PROPN
ijassa-899	324	2	(	(	PUNCT
ijassa-899	324	3	x−1ax−1	x−1ax−1	PROPN
ijassa-899	324	4	)	)	PUNCT
ijassa-899	324	5	t	t	NOUN
ijassa-899	324	6	,	,	PUNCT
ijassa-899	324	7	get	get	VERB
ijassa-899	324	8	∂fx	∂fx	NOUN
ijassa-899	324	9	,	,	PUNCT
ijassa-899	324	10	ν(w	ν(w	PROPN
ijassa-899	324	11	,	,	PUNCT
ijassa-899	324	12	µ,σ	µ,σ	INTJ
ijassa-899	324	13	)	)	PUNCT
ijassa-899	325	1	∂σj	∂σj	PROPN
ijassa-899	325	2	=	=	SYM
ijassa-899	325	3	−1	−1	NOUN
ijassa-899	325	4	2	2	NUM
ijassa-899	325	5	n∑	n∑	NOUN
ijassa-899	325	6	i=1	i=1	PROPN
ijassa-899	325	7	k∑	k∑	PROPN
ijassa-899	325	8	j=1	j=1	PROPN
ijassa-899	325	9	rij	rij	X
ijassa-899	325	10	[	[	PUNCT
ijassa-899	325	11	∂	∂	NUM
ijassa-899	325	12	∂σj	∂σj	PROPN
ijassa-899	325	13	ln	ln	PROPN
ijassa-899	325	14	det	det	PROPN
ijassa-899	325	15	σj	σj	VERB
ijassa-899	325	16	+	+	X
ijassa-899	325	17	ci	ci	PROPN
ijassa-899	325	18	∂	∂	NOUN
ijassa-899	325	19	∂σj	∂σj	PROPN
ijassa-899	325	20	(	(	PUNCT
ijassa-899	325	21	xi	xi	PROPN
ijassa-899	325	22	−	−	PROPN
ijassa-899	325	23	µj)tς−1	µj)tς−1	PROPN
ijassa-899	325	24	j	j	PROPN
ijassa-899	325	25	(	(	PUNCT
ijassa-899	325	26	xi	xi	PROPN
ijassa-899	325	27	−	−	PROPN
ijassa-899	325	28	µj	µj	PROPN
ijassa-899	325	29	)	)	PUNCT
ijassa-899	325	30	]	]	PUNCT
ijassa-899	326	1	=	=	PUNCT
ijassa-899	326	2	=	=	SYM
ijassa-899	326	3	−1	−1	NOUN
ijassa-899	326	4	2	2	NUM
ijassa-899	326	5	n∑	n∑	NOUN
ijassa-899	326	6	i=1	i=1	PROPN
ijassa-899	326	7	rij	rij	PROPN
ijassa-899	326	8	(	(	PUNCT
ijassa-899	326	9	σ−1	σ−1	INTJ
ijassa-899	326	10	j	j	PROPN
ijassa-899	327	1	−	−	PROPN
ijassa-899	327	2	ciς−1	ciς−1	PROPN
ijassa-899	327	3	j	j	PROPN
ijassa-899	327	4	(	(	PUNCT
ijassa-899	327	5	xi	xi	X
ijassa-899	327	6	−	−	PROPN
ijassa-899	327	7	µj)(xi	µj)(xi	PUNCT
ijassa-899	327	8	−	−	PROPN
ijassa-899	327	9	µj)tς−1	µj)tς−1	PROPN
ijassa-899	327	10	j	j	PROPN
ijassa-899	327	11	)	)	PUNCT
ijassa-899	327	12	=	=	PUNCT
ijassa-899	328	1	0	0	X
ijassa-899	328	2	.	.	PUNCT
ijassa-899	329	1	copyright	copyright	NOUN
ijassa-899	329	2	©	©	PROPN
ijassa-899	329	3	2020	2020	NUM
ijassa-899	329	4	assa	assa	NOUN
ijassa-899	329	5	.	.	PUNCT
ijassa-899	330	1	adv	adv	PROPN
ijassa-899	330	2	syst	syst	PROPN
ijassa-899	330	3	sci	sci	PROPN
ijassa-899	330	4	appl	appl	PROPN
ijassa-899	330	5	(	(	PUNCT
ijassa-899	330	6	2020	2020	NUM
ijassa-899	330	7	)	)	PUNCT
ijassa-899	330	8	108	108	NUM
ijassa-899	330	9	n.a	n.a	PROPN
ijassa-899	330	10	.	.	PROPN
ijassa-899	330	11	volkov	volkov	PROPN
ijassa-899	330	12	,	,	PUNCT
ijassa-899	330	13	e.yu	e.yu	PROPN
ijassa-899	330	14	.	.	PROPN
ijassa-899	330	15	dakhova	dakhova	PROPN
ijassa-899	330	16	,	,	PUNCT
ijassa-899	330	17	s.a	s.a	PROPN
ijassa-899	330	18	.	.	PROPN
ijassa-899	330	19	budennyy	budennyy	PROPN
ijassa-899	330	20	,	,	PUNCT
ijassa-899	330	21	a.m.	a.m.	PROPN
ijassa-899	330	22	andrianova	andrianova	PROPN
ijassa-899	331	1	multiplying	multiply	VERB
ijassa-899	331	2	both	both	DET
ijassa-899	331	3	parts	part	NOUN
ijassa-899	331	4	of	of	ADP
ijassa-899	331	5	the	the	DET
ijassa-899	331	6	last	last	ADJ
ijassa-899	331	7	equation	equation	NOUN
ijassa-899	331	8	by	by	ADP
ijassa-899	331	9	σj	σj	NOUN
ijassa-899	331	10	we	we	PRON
ijassa-899	331	11	get	get	VERB
ijassa-899	331	12	n∑	n∑	PROPN
ijassa-899	331	13	i=1	i=1	PROPN
ijassa-899	331	14	rijσj	rijσj	NOUN
ijassa-899	332	1	=	=	PUNCT
ijassa-899	332	2	n∑	n∑	PROPN
ijassa-899	332	3	i=1	i=1	PROPN
ijassa-899	332	4	rijci	rijci	PROPN
ijassa-899	332	5	(	(	PUNCT
ijassa-899	332	6	xi	xi	X
ijassa-899	332	7	−	−	PROPN
ijassa-899	332	8	µj)(xi	µj)(xi	PUNCT
ijassa-899	332	9	−	−	PROPN
ijassa-899	332	10	µj)t	µj)t	PRON
ijassa-899	332	11	,	,	PUNCT
ijassa-899	332	12	σj	σj	ADJ
ijassa-899	332	13	=	=	PROPN
ijassa-899	332	14	n∑	n∑	PROPN
ijassa-899	332	15	i=1	i=1	PROPN
ijassa-899	332	16	rijci	rijci	PROPN
ijassa-899	332	17	(	(	PUNCT
ijassa-899	332	18	xi	xi	X
ijassa-899	332	19	−	−	PROPN
ijassa-899	333	1	µj)(xi	µj)(xi	PUNCT
ijassa-899	333	2	−	−	PROPN
ijassa-899	333	3	µj)t	µj)t	SYM
ijassa-899	333	4	/	/	SYM
ijassa-899	333	5	n∑	n∑	NOUN
ijassa-899	333	6	i=1	i=1	PROPN
ijassa-899	333	7	rij	rij	PROPN
ijassa-899	333	8	.	.	PUNCT
ijassa-899	334	1	4.4	4.4	NUM
ijassa-899	334	2	.	.	PUNCT
ijassa-899	334	3	variational	variational	ADJ
ijassa-899	334	4	lower	lower	ADV
ijassa-899	334	5	bound	bind	VERB
ijassa-899	334	6	and	and	CCONJ
ijassa-899	334	7	convergence	convergence	NOUN
ijassa-899	334	8	of	of	ADP
ijassa-899	334	9	the	the	DET
ijassa-899	334	10	method	method	ADJ
ijassa-899	334	11	iterations	iteration	NOUN
ijassa-899	334	12	of	of	ADP
ijassa-899	334	13	the	the	DET
ijassa-899	334	14	em	em	PROPN
ijassa-899	334	15	algorithm	algorithm	NOUN
ijassa-899	334	16	continue	continue	VERB
ijassa-899	334	17	until	until	SCONJ
ijassa-899	334	18	the	the	DET
ijassa-899	334	19	convergence	convergence	NOUN
ijassa-899	334	20	of	of	ADP
ijassa-899	334	21	the	the	DET
ijassa-899	334	22	variational	variational	ADV
ijassa-899	334	23	lower	lower	ADV
ijassa-899	334	24	bound	bind	VERB
ijassa-899	334	25	[	[	PUNCT
ijassa-899	334	26	6	6	NUM
ijassa-899	334	27	]	]	X
ijassa-899	334	28	l(w	l(w	PROPN
ijassa-899	334	29	,	,	PUNCT
ijassa-899	334	30	µ,σ	µ,σ	INTJ
ijassa-899	334	31	,	,	PUNCT
ijassa-899	334	32	r	r	NOUN
ijassa-899	334	33	,	,	PUNCT
ijassa-899	334	34	a	a	DET
ijassa-899	334	35	,	,	PUNCT
ijassa-899	334	36	b	b	NOUN
ijassa-899	334	37	)	)	PUNCT
ijassa-899	334	38	=	=	SYM
ijassa-899	334	39	er	er	INTJ
ijassa-899	334	40	,	,	PUNCT
ijassa-899	334	41	γ	γ	PROPN
ijassa-899	334	42	ln	ln	ADJ
ijassa-899	334	43	p(x	p(x	PROPN
ijassa-899	334	44	,	,	PUNCT
ijassa-899	334	45	t	t	PROPN
ijassa-899	334	46	,	,	PUNCT
ijassa-899	334	47	y	y	PROPN
ijassa-899	334	48	|w	|w	PROPN
ijassa-899	334	49	,	,	PUNCT
ijassa-899	334	50	µ,σ	µ,σ	PROPN
ijassa-899	334	51	,	,	PUNCT
ijassa-899	334	52	ν)−	ν)−	PROPN
ijassa-899	334	53	er	er	INTJ
ijassa-899	334	54	ln	ln	ADJ
ijassa-899	334	55	r(t	r(t	NOUN
ijassa-899	334	56	)	)	PUNCT
ijassa-899	335	1	−	−	ADP
ijassa-899	335	2	eγ	eγ	ADP
ijassa-899	335	3	ln	ln	PROPN
ijassa-899	335	4	γ(y	γ(y	PROPN
ijassa-899	335	5	)	)	PUNCT
ijassa-899	335	6	.	.	PUNCT
ijassa-899	336	1	let	let	VERB
ijassa-899	336	2	us	we	PRON
ijassa-899	336	3	write	write	VERB
ijassa-899	336	4	each	each	DET
ijassa-899	336	5	summand	summand	NOUN
ijassa-899	336	6	separately	separately	ADV
ijassa-899	336	7	er	er	INTJ
ijassa-899	336	8	,	,	PUNCT
ijassa-899	336	9	γ	γ	PROPN
ijassa-899	336	10	ln	ln	ADJ
ijassa-899	336	11	p(x	p(x	PROPN
ijassa-899	336	12	,	,	PUNCT
ijassa-899	336	13	t	t	PROPN
ijassa-899	336	14	,	,	PUNCT
ijassa-899	336	15	y	y	PROPN
ijassa-899	336	16	|w	|w	PROPN
ijassa-899	336	17	,	,	PUNCT
ijassa-899	336	18	µ,σ	µ,σ	PROPN
ijassa-899	336	19	,	,	PUNCT
ijassa-899	336	20	ν	ν	NOUN
ijassa-899	336	21	)	)	PUNCT
ijassa-899	336	22	=	=	PUNCT
ijassa-899	337	1	=	=	PUNCT
ijassa-899	337	2	n∑	n∑	PROPN
ijassa-899	337	3	i=1	i=1	PROPN
ijassa-899	338	1	k∑	k∑	PROPN
ijassa-899	338	2	j=1	j=1	PROPN
ijassa-899	338	3	ertij	ertij	PROPN
ijassa-899	338	4	[	[	PUNCT
ijassa-899	338	5	lnwj	lnwj	NOUN
ijassa-899	339	1	−	−	PROPN
ijassa-899	340	1	d	d	PROPN
ijassa-899	340	2	2	2	NUM
ijassa-899	340	3	ln	ln	NOUN
ijassa-899	340	4	2π	2π	NOUN
ijassa-899	340	5	+	+	CCONJ
ijassa-899	340	6	d	d	SYM
ijassa-899	340	7	2	2	NUM
ijassa-899	340	8	eγ	eγ	ADP
ijassa-899	340	9	lnyi	lnyi	NOUN
ijassa-899	340	10	−	−	PROPN
ijassa-899	340	11	1	1	NUM
ijassa-899	340	12	2	2	NUM
ijassa-899	340	13	ln	ln	NOUN
ijassa-899	340	14	det	det	NOUN
ijassa-899	340	15	σj−	σj−	PUNCT
ijassa-899	340	16	−1	−1	NOUN
ijassa-899	340	17	2	2	NUM
ijassa-899	340	18	(	(	PUNCT
ijassa-899	340	19	xi	xi	X
ijassa-899	340	20	−	−	PROPN
ijassa-899	340	21	µj)tς−1	µj)tς−1	PROPN
ijassa-899	340	22	j	j	PROPN
ijassa-899	340	23	(	(	PUNCT
ijassa-899	340	24	xi	xi	INTJ
ijassa-899	340	25	−	−	PROPN
ijassa-899	340	26	µj)eγyi	µj)eγyi	ADV
ijassa-899	340	27	+	+	NUM
ijassa-899	340	28	ν	ν	NOUN
ijassa-899	340	29	2	2	NUM
ijassa-899	340	30	ln	ln	NOUN
ijassa-899	340	31	ν	ν	NOUN
ijassa-899	340	32	2	2	NUM
ijassa-899	340	33	−	−	NOUN
ijassa-899	340	34	γ	γ	NOUN
ijassa-899	340	35	(	(	PUNCT
ijassa-899	340	36	ν	ν	PROPN
ijassa-899	340	37	2	2	NUM
ijassa-899	340	38	)	)	PUNCT
ijassa-899	340	39	+	+	CCONJ
ijassa-899	340	40	(	(	PUNCT
ijassa-899	340	41	ν	ν	NOUN
ijassa-899	340	42	2	2	NUM
ijassa-899	340	43	−	−	NOUN
ijassa-899	340	44	1	1	NUM
ijassa-899	340	45	)	)	PUNCT
ijassa-899	340	46	eγ	eγ	ADP
ijassa-899	340	47	lnyi	lnyi	NOUN
ijassa-899	340	48	−	−	PROPN
ijassa-899	340	49	ν	ν	NOUN
ijassa-899	340	50	2	2	NUM
ijassa-899	340	51	eγyi	eγyi	NOUN
ijassa-899	340	52	]	]	PUNCT
ijassa-899	341	1	=	=	PUNCT
ijassa-899	341	2	=	=	PUNCT
ijassa-899	341	3	n∑	n∑	PROPN
ijassa-899	341	4	i=1	i=1	PROPN
ijassa-899	341	5	k∑	k∑	PROPN
ijassa-899	341	6	j=1	j=1	PROPN
ijassa-899	341	7	rij	rij	X
ijassa-899	341	8	[	[	PUNCT
ijassa-899	341	9	lnwj	lnwj	NOUN
ijassa-899	341	10	−	−	PROPN
ijassa-899	342	1	d	d	PROPN
ijassa-899	342	2	2	2	NUM
ijassa-899	342	3	ln	ln	NOUN
ijassa-899	342	4	2π	2π	NOUN
ijassa-899	342	5	−	−	NOUN
ijassa-899	342	6	1	1	NUM
ijassa-899	342	7	2	2	NUM
ijassa-899	342	8	ln	ln	NOUN
ijassa-899	342	9	det	det	X
ijassa-899	342	10	σj−	σj−	PUNCT
ijassa-899	342	11	−	−	PROPN
ijassa-899	342	12	bi	bi	NOUN
ijassa-899	342	13	2ai	2ai	NOUN
ijassa-899	342	14	[	[	PUNCT
ijassa-899	342	15	ν	ν	X
ijassa-899	342	16	+	+	CCONJ
ijassa-899	342	17	(	(	PUNCT
ijassa-899	342	18	xi	xi	INTJ
ijassa-899	342	19	−	−	PROPN
ijassa-899	342	20	µj)tς−1	µj)tς−1	PROPN
ijassa-899	342	21	j	j	PROPN
ijassa-899	342	22	(	(	PUNCT
ijassa-899	342	23	xi	xi	PROPN
ijassa-899	342	24	−	−	PROPN
ijassa-899	342	25	µj	µj	PROPN
ijassa-899	342	26	)	)	PUNCT
ijassa-899	342	27	]	]	PUNCT
ijassa-899	343	1	+	+	CCONJ
ijassa-899	343	2	ν	ν	X
ijassa-899	343	3	2	2	NUM
ijassa-899	343	4	ln	ln	NOUN
ijassa-899	343	5	ν	ν	NOUN
ijassa-899	343	6	2	2	NUM
ijassa-899	343	7	−	−	NOUN
ijassa-899	343	8	γ	γ	NOUN
ijassa-899	343	9	(	(	PUNCT
ijassa-899	343	10	ν	ν	PROPN
ijassa-899	343	11	2	2	NUM
ijassa-899	343	12	)	)	PUNCT
ijassa-899	343	13	+	+	CCONJ
ijassa-899	343	14	(	(	PUNCT
ijassa-899	343	15	ν	ν	X
ijassa-899	343	16	+	+	CCONJ
ijassa-899	343	17	d	d	SYM
ijassa-899	343	18	2	2	NUM
ijassa-899	343	19	−	−	NOUN
ijassa-899	343	20	1	1	NUM
ijassa-899	343	21	)	)	PUNCT
ijassa-899	343	22	(	(	PUNCT
ijassa-899	343	23	ψ(bi)−	ψ(bi)−	NUM
ijassa-899	343	24	ln	ln	PROPN
ijassa-899	343	25	ai	ai	NOUN
ijassa-899	343	26	)	)	PUNCT
ijassa-899	343	27	]	]	PUNCT
ijassa-899	343	28	,	,	PUNCT
ijassa-899	343	29	er	er	INTJ
ijassa-899	343	30	ln	ln	ADJ
ijassa-899	343	31	r(t	r(t	NOUN
ijassa-899	343	32	)	)	PUNCT
ijassa-899	344	1	=	=	PUNCT
ijassa-899	345	1	n∑	n∑	PROPN
ijassa-899	345	2	i=1	i=1	PROPN
ijassa-899	346	1	k∑	k∑	PROPN
ijassa-899	347	1	j=1	j=1	PROPN
ijassa-899	347	2	rij	rij	PROPN
ijassa-899	347	3	ln	ln	X
ijassa-899	347	4	rij	rij	PROPN
ijassa-899	347	5	,	,	PUNCT
ijassa-899	347	6	eγ	eγ	ADP
ijassa-899	347	7	ln	ln	PROPN
ijassa-899	347	8	γ(y	γ(y	PROPN
ijassa-899	347	9	)	)	PUNCT
ijassa-899	348	1	=	=	PUNCT
ijassa-899	349	1	n∑	n∑	INTJ
ijassa-899	349	2	i=1	i=1	X
ijassa-899	349	3	eγ	eγ	X
ijassa-899	349	4	ln	ln	ADJ
ijassa-899	349	5	γ(yi	γ(yi	PROPN
ijassa-899	349	6	)	)	PUNCT
ijassa-899	350	1	=	=	SYM
ijassa-899	351	1	n∑	n∑	NOUN
ijassa-899	351	2	i=1	i=1	X
ijassa-899	352	1	[	[	X
ijassa-899	352	2	bi	bi	NOUN
ijassa-899	352	3	ln	ln	NOUN
ijassa-899	352	4	ai	ai	PROPN
ijassa-899	352	5	−	−	PROPN
ijassa-899	352	6	ln	ln	ADJ
ijassa-899	352	7	γ(bi	γ(bi	PROPN
ijassa-899	352	8	)	)	PUNCT
ijassa-899	353	1	+	+	CCONJ
ijassa-899	353	2	(	(	PUNCT
ijassa-899	353	3	bi	bi	NOUN
ijassa-899	353	4	−	−	PROPN
ijassa-899	353	5	1)eγ	1)eγ	NUM
ijassa-899	353	6	lnyi	lnyi	NOUN
ijassa-899	353	7	−	−	NOUN
ijassa-899	353	8	aieγyi	aieγyi	ADV
ijassa-899	353	9	]	]	X
ijassa-899	354	1	=	=	PUNCT
ijassa-899	354	2	=	=	PUNCT
ijassa-899	354	3	n∑	n∑	NOUN
ijassa-899	354	4	i=1	i=1	X
ijassa-899	355	1	[	[	X
ijassa-899	355	2	bi	bi	NOUN
ijassa-899	355	3	ln	ln	NOUN
ijassa-899	355	4	ai	ai	PROPN
ijassa-899	355	5	−	−	PROPN
ijassa-899	355	6	ln	ln	ADJ
ijassa-899	355	7	γ(bi	γ(bi	PROPN
ijassa-899	355	8	)	)	PUNCT
ijassa-899	356	1	+	+	CCONJ
ijassa-899	356	2	(	(	PUNCT
ijassa-899	356	3	bi	bi	ADJ
ijassa-899	356	4	−	−	PROPN
ijassa-899	356	5	1	1	NUM
ijassa-899	356	6	)	)	PUNCT
ijassa-899	356	7	(	(	PUNCT
ijassa-899	356	8	ψ(bi)−	ψ(bi)−	X
ijassa-899	356	9	ln	ln	ADJ
ijassa-899	356	10	ai)−	ai)−	NOUN
ijassa-899	356	11	bi	bi	NOUN
ijassa-899	356	12	]	]	PUNCT
ijassa-899	356	13	.	.	PUNCT
ijassa-899	357	1	let	let	VERB
ijassa-899	357	2	us	we	PRON
ijassa-899	357	3	study	study	VERB
ijassa-899	357	4	the	the	DET
ijassa-899	357	5	convergence	convergence	NOUN
ijassa-899	357	6	of	of	ADP
ijassa-899	357	7	the	the	DET
ijassa-899	357	8	method	method	NOUN
ijassa-899	357	9	.	.	PUNCT
ijassa-899	358	1	the	the	DET
ijassa-899	358	2	standard	standard	NOUN
ijassa-899	358	3	em	em	PRON
ijassa-899	358	4	algorithm	algorithm	NOUN
ijassa-899	358	5	is	be	AUX
ijassa-899	358	6	based	base	VERB
ijassa-899	358	7	on	on	ADP
ijassa-899	358	8	the	the	DET
ijassa-899	358	9	formula	formula	NOUN
ijassa-899	358	10	(	(	PUNCT
ijassa-899	358	11	see	see	VERB
ijassa-899	358	12	[	[	X
ijassa-899	358	13	6	6	NUM
ijassa-899	358	14	]	]	SYM
ijassa-899	358	15	)	)	PUNCT
ijassa-899	359	1	logl(w	logl(w	PROPN
ijassa-899	359	2	,	,	PUNCT
ijassa-899	359	3	µ,σ	µ,σ	NOUN
ijassa-899	359	4	)	)	PUNCT
ijassa-899	359	5	=	=	SYM
ijassa-899	360	1	l(w	l(w	PROPN
ijassa-899	360	2	,	,	PUNCT
ijassa-899	360	3	µ,σ	µ,σ	INTJ
ijassa-899	360	4	,	,	PUNCT
ijassa-899	360	5	q	q	NOUN
ijassa-899	360	6	)	)	PUNCT
ijassa-899	360	7	+	+	NOUN
ijassa-899	360	8	kl(q	kl(q	NOUN
ijassa-899	360	9	,	,	PUNCT
ijassa-899	360	10	pw,µ,σ(t	pw,µ,σ(t	NOUN
ijassa-899	360	11	,	,	PUNCT
ijassa-899	360	12	y|x	y|x	NOUN
ijassa-899	360	13	)	)	PUNCT
ijassa-899	360	14	)	)	PUNCT
ijassa-899	360	15	,	,	PUNCT
ijassa-899	360	16	where	where	SCONJ
ijassa-899	360	17	q	q	NOUN
ijassa-899	360	18	is	be	AUX
ijassa-899	360	19	a	a	DET
ijassa-899	360	20	distribution	distribution	NOUN
ijassa-899	360	21	of	of	ADP
ijassa-899	360	22	the	the	DET
ijassa-899	360	23	vector	vector	NOUN
ijassa-899	360	24	(	(	PUNCT
ijassa-899	360	25	t	t	PROPN
ijassa-899	360	26	,	,	PUNCT
ijassa-899	360	27	y	y	PROPN
ijassa-899	360	28	)	)	PUNCT
ijassa-899	360	29	,	,	PUNCT
ijassa-899	360	30	kl	kl	PROPN
ijassa-899	360	31	is	be	AUX
ijassa-899	360	32	kullback	kullback	NOUN
ijassa-899	360	33	-	-	PUNCT
ijassa-899	360	34	leibler	leibler	NOUN
ijassa-899	360	35	divergence	divergence	NOUN
ijassa-899	360	36	.	.	PUNCT
ijassa-899	361	1	here	here	ADV
ijassa-899	361	2	,	,	PUNCT
ijassa-899	361	3	for	for	ADP
ijassa-899	361	4	the	the	DET
ijassa-899	361	5	variational	variational	ADJ
ijassa-899	361	6	lower	lower	ADV
ijassa-899	361	7	bound	bind	VERB
ijassa-899	361	8	,	,	PUNCT
ijassa-899	361	9	we	we	PRON
ijassa-899	361	10	use	use	VERB
ijassa-899	361	11	the	the	DET
ijassa-899	361	12	notation	notation	NOUN
ijassa-899	361	13	l(w	l(w	PROPN
ijassa-899	361	14	,	,	PUNCT
ijassa-899	361	15	µ,σ	µ,σ	INTJ
ijassa-899	361	16	,	,	PUNCT
ijassa-899	361	17	q	q	NOUN
ijassa-899	361	18	)	)	PUNCT
ijassa-899	361	19	since	since	SCONJ
ijassa-899	361	20	,	,	PUNCT
ijassa-899	361	21	in	in	ADP
ijassa-899	361	22	general	general	ADJ
ijassa-899	361	23	,	,	PUNCT
ijassa-899	361	24	q	q	PROPN
ijassa-899	361	25	does	do	AUX
ijassa-899	361	26	not	not	PART
ijassa-899	361	27	depend	depend	VERB
ijassa-899	361	28	on	on	ADP
ijassa-899	361	29	parameters	parameter	NOUN
ijassa-899	361	30	.	.	PUNCT
ijassa-899	362	1	in	in	ADP
ijassa-899	362	2	order	order	NOUN
ijassa-899	362	3	to	to	PART
ijassa-899	362	4	maximize	maximize	VERB
ijassa-899	362	5	the	the	DET
ijassa-899	362	6	likelihood	likelihood	NOUN
ijassa-899	362	7	function	function	NOUN
ijassa-899	362	8	,	,	PUNCT
ijassa-899	362	9	the	the	DET
ijassa-899	362	10	variational	variational	ADJ
ijassa-899	362	11	lower	lower	ADV
ijassa-899	362	12	bound	bind	VERB
ijassa-899	362	13	is	be	AUX
ijassa-899	362	14	maximized	maximize	VERB
ijassa-899	362	15	at	at	ADP
ijassa-899	362	16	each	each	DET
ijassa-899	362	17	step	step	NOUN
ijassa-899	362	18	,	,	PUNCT
ijassa-899	362	19	at	at	ADP
ijassa-899	362	20	the	the	DET
ijassa-899	362	21	e	e	NOUN
ijassa-899	362	22	-	-	NOUN
ijassa-899	362	23	step	step	NOUN
ijassa-899	362	24	by	by	ADP
ijassa-899	362	25	q	q	X
ijassa-899	362	26	,	,	PUNCT
ijassa-899	362	27	at	at	ADP
ijassa-899	362	28	the	the	DET
ijassa-899	362	29	m	m	NOUN
ijassa-899	362	30	-	-	NOUN
ijassa-899	362	31	step	step	NOUN
ijassa-899	362	32	by	by	ADP
ijassa-899	362	33	the	the	DET
ijassa-899	362	34	parameters	parameter	NOUN
ijassa-899	362	35	w	w	PROPN
ijassa-899	362	36	,	,	PUNCT
ijassa-899	362	37	µ,σ	µ,σ	PROPN
ijassa-899	362	38	.	.	PUNCT
ijassa-899	363	1	copyright	copyright	NOUN
ijassa-899	363	2	©	©	PROPN
ijassa-899	363	3	2020	2020	NUM
ijassa-899	363	4	assa	assa	NOUN
ijassa-899	363	5	.	.	PUNCT
ijassa-899	364	1	adv	adv	PROPN
ijassa-899	364	2	syst	syst	PROPN
ijassa-899	364	3	sci	sci	PROPN
ijassa-899	364	4	appl	appl	PROPN
ijassa-899	364	5	(	(	PUNCT
ijassa-899	364	6	2020	2020	NUM
ijassa-899	364	7	)	)	PUNCT
ijassa-899	364	8	student	student	NOUN
ijassa-899	364	9	mixture	mixture	NOUN
ijassa-899	364	10	and	and	CCONJ
ijassa-899	364	11	its	its	PRON
ijassa-899	364	12	machine	machine	NOUN
ijassa-899	364	13	learning	learn	VERB
ijassa-899	364	14	applications	application	NOUN
ijassa-899	364	15	to	to	ADP
ijassa-899	364	16	pvt	pvt	PROPN
ijassa-899	364	17	properties	property	NOUN
ijassa-899	364	18	109	109	NUM
ijassa-899	364	19	thus	thus	ADV
ijassa-899	364	20	,	,	PUNCT
ijassa-899	364	21	its	its	PRON
ijassa-899	364	22	values	value	NOUN
ijassa-899	364	23	do	do	AUX
ijassa-899	364	24	not	not	PART
ijassa-899	364	25	decrease	decrease	VERB
ijassa-899	364	26	.	.	PUNCT
ijassa-899	365	1	maximization	maximization	NOUN
ijassa-899	365	2	at	at	ADP
ijassa-899	365	3	the	the	DET
ijassa-899	365	4	e	e	NOUN
ijassa-899	365	5	-	-	NOUN
ijassa-899	365	6	step	step	NOUN
ijassa-899	365	7	is	be	AUX
ijassa-899	365	8	equivalent	equivalent	ADJ
ijassa-899	365	9	to	to	ADP
ijassa-899	365	10	minimizing	minimize	VERB
ijassa-899	365	11	the	the	DET
ijassa-899	365	12	divergence	divergence	NOUN
ijassa-899	365	13	by	by	ADP
ijassa-899	365	14	q	q	PROPN
ijassa-899	365	15	,	,	PUNCT
ijassa-899	365	16	which	which	PRON
ijassa-899	365	17	is	be	AUX
ijassa-899	365	18	equivalent	equivalent	ADJ
ijassa-899	365	19	to	to	ADP
ijassa-899	365	20	choosing	choose	VERB
ijassa-899	365	21	q	q	NOUN
ijassa-899	365	22	=	=	NOUN
ijassa-899	365	23	pw,µ,σ(t	pw,µ,σ(t	NOUN
ijassa-899	365	24	,	,	PUNCT
ijassa-899	365	25	y|x	y|x	NOUN
ijassa-899	365	26	)	)	PUNCT
ijassa-899	365	27	,	,	PUNCT
ijassa-899	365	28	where	where	SCONJ
ijassa-899	365	29	the	the	DET
ijassa-899	365	30	divergence	divergence	NOUN
ijassa-899	365	31	is	be	AUX
ijassa-899	365	32	exactly	exactly	ADV
ijassa-899	365	33	zero	zero	NUM
ijassa-899	365	34	.	.	PUNCT
ijassa-899	366	1	thus	thus	ADV
ijassa-899	366	2	,	,	PUNCT
ijassa-899	366	3	the	the	DET
ijassa-899	366	4	variational	variational	NOUN
ijassa-899	366	5	lower	lower	ADV
ijassa-899	366	6	bound	bind	VERB
ijassa-899	366	7	after	after	ADP
ijassa-899	366	8	the	the	DET
ijassa-899	366	9	e	e	NOUN
ijassa-899	366	10	-	-	NOUN
ijassa-899	366	11	step	step	NOUN
ijassa-899	366	12	is	be	AUX
ijassa-899	366	13	equal	equal	ADJ
ijassa-899	366	14	to	to	ADP
ijassa-899	366	15	the	the	DET
ijassa-899	366	16	logarithm	logarithm	NOUN
ijassa-899	366	17	of	of	ADP
ijassa-899	366	18	the	the	DET
ijassa-899	366	19	likelihood	likelihood	NOUN
ijassa-899	366	20	function	function	NOUN
ijassa-899	366	21	logl(w	logl(w	PROPN
ijassa-899	366	22	,	,	PUNCT
ijassa-899	366	23	µ,σ).therefore	µ,σ).therefore	ADP
ijassa-899	366	24	the	the	DET
ijassa-899	366	25	likelihood	likelihood	NOUN
ijassa-899	366	26	function	function	NOUN
ijassa-899	366	27	also	also	ADV
ijassa-899	366	28	does	do	AUX
ijassa-899	366	29	not	not	PART
ijassa-899	366	30	decrease	decrease	VERB
ijassa-899	366	31	and	and	CCONJ
ijassa-899	366	32	converges	converge	VERB
ijassa-899	366	33	to	to	ADP
ijassa-899	366	34	the	the	DET
ijassa-899	366	35	local	local	ADJ
ijassa-899	366	36	maximum	maximum	NOUN
ijassa-899	366	37	.	.	PUNCT
ijassa-899	367	1	for	for	ADP
ijassa-899	367	2	the	the	DET
ijassa-899	367	3	modification	modification	NOUN
ijassa-899	367	4	of	of	ADP
ijassa-899	367	5	the	the	DET
ijassa-899	367	6	em	em	PROPN
ijassa-899	367	7	algorithm	algorithm	NOUN
ijassa-899	367	8	under	under	ADP
ijassa-899	367	9	consideration	consideration	NOUN
ijassa-899	367	10	,	,	PUNCT
ijassa-899	367	11	the	the	DET
ijassa-899	367	12	formula	formula	NOUN
ijassa-899	367	13	takes	take	VERB
ijassa-899	367	14	the	the	DET
ijassa-899	367	15	form	form	NOUN
ijassa-899	367	16	logl(w	logl(w	NOUN
ijassa-899	367	17	,	,	PUNCT
ijassa-899	367	18	µ,σ	µ,σ	NOUN
ijassa-899	367	19	)	)	PUNCT
ijassa-899	367	20	=	=	SYM
ijassa-899	368	1	l(w	l(w	PROPN
ijassa-899	368	2	,	,	PUNCT
ijassa-899	368	3	µ,σ	µ,σ	INTJ
ijassa-899	368	4	,	,	PUNCT
ijassa-899	368	5	r	r	NOUN
ijassa-899	368	6	,	,	PUNCT
ijassa-899	368	7	a	a	DET
ijassa-899	368	8	,	,	PUNCT
ijassa-899	368	9	b	b	NOUN
ijassa-899	368	10	)	)	PUNCT
ijassa-899	368	11	+	+	NOUN
ijassa-899	368	12	kl(r	kl(r	X
ijassa-899	368	13	×	×	PROPN
ijassa-899	368	14	γ	γ	NOUN
ijassa-899	368	15	,	,	PUNCT
ijassa-899	368	16	pw,µ,σ(t	pw,µ,σ(t	NOUN
ijassa-899	368	17	,	,	PUNCT
ijassa-899	368	18	y|x	y|x	NOUN
ijassa-899	368	19	)	)	PUNCT
ijassa-899	368	20	)	)	PUNCT
ijassa-899	368	21	.	.	PUNCT
ijassa-899	369	1	variational	variational	ADJ
ijassa-899	369	2	output	output	NOUN
ijassa-899	369	3	at	at	ADP
ijassa-899	369	4	the	the	DET
ijassa-899	369	5	e	e	NOUN
ijassa-899	369	6	-	-	NOUN
ijassa-899	369	7	step	step	NOUN
ijassa-899	369	8	minimizes	minimize	NOUN
ijassa-899	369	9	divergence	divergence	NOUN
ijassa-899	369	10	.	.	PUNCT
ijassa-899	370	1	however	however	ADV
ijassa-899	370	2	in	in	ADP
ijassa-899	370	3	the	the	DET
ijassa-899	370	4	class	class	NOUN
ijassa-899	370	5	of	of	ADP
ijassa-899	370	6	distributions	distribution	NOUN
ijassa-899	370	7	under	under	ADP
ijassa-899	370	8	consideration	consideration	NOUN
ijassa-899	370	9	,	,	PUNCT
ijassa-899	370	10	where	where	SCONJ
ijassa-899	370	11	the	the	DET
ijassa-899	370	12	distributions	distribution	NOUN
ijassa-899	370	13	of	of	ADP
ijassa-899	370	14	t	t	PROPN
ijassa-899	370	15	and	and	CCONJ
ijassa-899	370	16	y	y	PROPN
ijassa-899	370	17	are	be	AUX
ijassa-899	370	18	conditionally	conditionally	ADV
ijassa-899	370	19	independent	independent	ADJ
ijassa-899	370	20	,	,	PUNCT
ijassa-899	370	21	the	the	DET
ijassa-899	370	22	zero	zero	NUM
ijassa-899	370	23	divergence	divergence	NOUN
ijassa-899	370	24	may	may	AUX
ijassa-899	370	25	not	not	PART
ijassa-899	370	26	be	be	AUX
ijassa-899	370	27	achieved	achieve	VERB
ijassa-899	370	28	.	.	PUNCT
ijassa-899	371	1	thus	thus	ADV
ijassa-899	371	2	,	,	PUNCT
ijassa-899	371	3	the	the	DET
ijassa-899	371	4	convergence	convergence	NOUN
ijassa-899	371	5	of	of	ADP
ijassa-899	371	6	l	l	NOUN
ijassa-899	371	7	to	to	ADP
ijassa-899	371	8	the	the	DET
ijassa-899	371	9	local	local	ADJ
ijassa-899	371	10	maximum	maximum	NOUN
ijassa-899	371	11	can	can	AUX
ijassa-899	371	12	not	not	PART
ijassa-899	371	13	be	be	AUX
ijassa-899	371	14	guaranteed	guarantee	VERB
ijassa-899	371	15	.	.	PUNCT
ijassa-899	372	1	in	in	ADP
ijassa-899	372	2	the	the	DET
ijassa-899	372	3	practical	practical	ADJ
ijassa-899	372	4	problems	problem	NOUN
ijassa-899	372	5	we	we	PRON
ijassa-899	372	6	are	be	AUX
ijassa-899	372	7	considering	consider	VERB
ijassa-899	372	8	,	,	PUNCT
ijassa-899	372	9	it	it	PRON
ijassa-899	372	10	is	be	AUX
ijassa-899	372	11	natural	natural	ADJ
ijassa-899	372	12	to	to	PART
ijassa-899	372	13	assume	assume	VERB
ijassa-899	372	14	that	that	SCONJ
ijassa-899	372	15	the	the	DET
ijassa-899	372	16	distributions	distribution	NOUN
ijassa-899	372	17	t	t	PROPN
ijassa-899	372	18	and	and	CCONJ
ijassa-899	372	19	y	y	PROPN
ijassa-899	372	20	are	be	AUX
ijassa-899	372	21	close	close	ADJ
ijassa-899	372	22	to	to	PART
ijassa-899	372	23	be	be	AUX
ijassa-899	372	24	conditionally	conditionally	ADV
ijassa-899	372	25	independent	independent	ADJ
ijassa-899	372	26	,	,	PUNCT
ijassa-899	372	27	that	that	PRON
ijassa-899	372	28	should	should	AUX
ijassa-899	372	29	provide	provide	VERB
ijassa-899	372	30	a	a	DET
ijassa-899	372	31	good	good	ADJ
ijassa-899	372	32	approximation	approximation	NOUN
ijassa-899	372	33	of	of	ADP
ijassa-899	372	34	the	the	DET
ijassa-899	372	35	local	local	ADJ
ijassa-899	372	36	maximum	maximum	NOUN
ijassa-899	372	37	using	use	VERB
ijassa-899	372	38	the	the	DET
ijassa-899	372	39	resulting	result	VERB
ijassa-899	372	40	estimation	estimation	NOUN
ijassa-899	372	41	.	.	PUNCT
ijassa-899	373	1	5	5	X
ijassa-899	373	2	.	.	X
ijassa-899	373	3	applications	application	NOUN
ijassa-899	373	4	of	of	ADP
ijassa-899	373	5	probabilistic	probabilistic	ADJ
ijassa-899	373	6	model	model	NOUN
ijassa-899	373	7	the	the	DET
ijassa-899	373	8	model	model	NOUN
ijassa-899	373	9	of	of	ADP
ijassa-899	373	10	a	a	DET
ijassa-899	373	11	mixture	mixture	NOUN
ijassa-899	373	12	distribution	distribution	NOUN
ijassa-899	373	13	allows	allow	VERB
ijassa-899	373	14	solving	solve	VERB
ijassa-899	373	15	various	various	ADJ
ijassa-899	373	16	machine	machine	NOUN
ijassa-899	373	17	learning	learn	VERB
ijassa-899	373	18	problems	problem	NOUN
ijassa-899	373	19	listed	list	VERB
ijassa-899	373	20	in	in	ADP
ijassa-899	373	21	the	the	DET
ijassa-899	373	22	introduction	introduction	NOUN
ijassa-899	373	23	and	and	CCONJ
ijassa-899	373	24	obtaining	obtain	VERB
ijassa-899	373	25	consistent	consistent	ADJ
ijassa-899	373	26	results	result	NOUN
ijassa-899	373	27	.	.	PUNCT
ijassa-899	374	1	let	let	VERB
ijassa-899	374	2	us	we	PRON
ijassa-899	374	3	describe	describe	VERB
ijassa-899	374	4	each	each	PRON
ijassa-899	374	5	of	of	ADP
ijassa-899	374	6	them	they	PRON
ijassa-899	374	7	in	in	ADP
ijassa-899	374	8	more	more	ADJ
ijassa-899	374	9	detail	detail	NOUN
ijassa-899	374	10	,	,	PUNCT
ijassa-899	374	11	assuming	assume	VERB
ijassa-899	374	12	a	a	DET
ijassa-899	374	13	mixture	mixture	NOUN
ijassa-899	374	14	model	model	NOUN
ijassa-899	374	15	with	with	ADP
ijassa-899	374	16	density	density	NOUN
ijassa-899	374	17	p(x	p(x	NOUN
ijassa-899	374	18	)	)	PUNCT
ijassa-899	374	19	=	=	SYM
ijassa-899	374	20	k∑	k∑	PROPN
ijassa-899	374	21	j=1	j=1	PROPN
ijassa-899	374	22	wjp(x|θj	wjp(x|θj	PROPN
ijassa-899	374	23	)	)	PUNCT
ijassa-899	374	24	,	,	PUNCT
ijassa-899	374	25	where	where	SCONJ
ijassa-899	374	26	θj	θj	NOUN
ijassa-899	374	27	is	be	AUX
ijassa-899	374	28	the	the	DET
ijassa-899	374	29	parameter	parameter	NOUN
ijassa-899	374	30	of	of	ADP
ijassa-899	374	31	the	the	DET
ijassa-899	374	32	jth	jth	PROPN
ijassa-899	374	33	component	component	NOUN
ijassa-899	374	34	distribution	distribution	NOUN
ijassa-899	374	35	(	(	PUNCT
ijassa-899	374	36	for	for	ADP
ijassa-899	374	37	example	example	NOUN
ijassa-899	374	38	,	,	PUNCT
ijassa-899	374	39	the	the	DET
ijassa-899	374	40	mean	mean	ADJ
ijassa-899	374	41	vector	vector	NOUN
ijassa-899	374	42	and	and	CCONJ
ijassa-899	374	43	the	the	DET
ijassa-899	374	44	covariance	covariance	NOUN
ijassa-899	374	45	matrix	matrix	NOUN
ijassa-899	374	46	)	)	PUNCT
ijassa-899	374	47	.	.	PUNCT
ijassa-899	375	1	5.1	5.1	NUM
ijassa-899	375	2	.	.	PUNCT
ijassa-899	376	1	clustering	cluster	VERB
ijassa-899	376	2	the	the	DET
ijassa-899	376	3	components	component	NOUN
ijassa-899	376	4	of	of	ADP
ijassa-899	376	5	the	the	DET
ijassa-899	376	6	mixture	mixture	NOUN
ijassa-899	376	7	can	can	AUX
ijassa-899	376	8	be	be	AUX
ijassa-899	376	9	considered	consider	VERB
ijassa-899	376	10	as	as	ADP
ijassa-899	376	11	overlapping	overlap	VERB
ijassa-899	376	12	clusters	cluster	NOUN
ijassa-899	376	13	.	.	PUNCT
ijassa-899	377	1	each	each	DET
ijassa-899	377	2	object	object	NOUN
ijassa-899	377	3	x	x	SYM
ijassa-899	377	4	∈	∈	PROPN
ijassa-899	377	5	rd	rd	NOUN
ijassa-899	377	6	can	can	AUX
ijassa-899	377	7	be	be	AUX
ijassa-899	377	8	assigned	assign	VERB
ijassa-899	377	9	to	to	ADP
ijassa-899	377	10	one	one	NUM
ijassa-899	377	11	of	of	ADP
ijassa-899	377	12	the	the	DET
ijassa-899	377	13	clusters	cluster	NOUN
ijassa-899	377	14	with	with	ADP
ijassa-899	377	15	some	some	DET
ijassa-899	377	16	probability	probability	NOUN
ijassa-899	377	17	.	.	PUNCT
ijassa-899	378	1	according	accord	VERB
ijassa-899	378	2	to	to	ADP
ijassa-899	378	3	claim	claim	NOUN
ijassa-899	378	4	3.2	3.2	NUM
ijassa-899	378	5	,	,	PUNCT
ijassa-899	378	6	the	the	DET
ijassa-899	378	7	conditional	conditional	ADJ
ijassa-899	378	8	probability	probability	NOUN
ijassa-899	378	9	that	that	SCONJ
ijassa-899	378	10	an	an	DET
ijassa-899	378	11	object	object	NOUN
ijassa-899	378	12	x	x	VERB
ijassa-899	378	13	corresponds	correspond	VERB
ijassa-899	378	14	to	to	ADP
ijassa-899	378	15	a	a	DET
ijassa-899	378	16	cluster	cluster	NOUN
ijassa-899	378	17	j	j	NOUN
ijassa-899	378	18	is	be	AUX
ijassa-899	378	19	pj(x	pj(x	PRON
ijassa-899	378	20	)	)	PUNCT
ijassa-899	378	21	=	=	SYM
ijassa-899	378	22	wjp(x|θj	wjp(x|θj	NOUN
ijassa-899	378	23	)	)	PUNCT
ijassa-899	378	24	k∑	k∑	NOUN
ijassa-899	379	1	s=1	s=1	X
ijassa-899	379	2	wsp(x|θs	wsp(x|θ	NOUN
ijassa-899	379	3	)	)	PUNCT
ijassa-899	379	4	.	.	PUNCT
ijassa-899	380	1	if	if	SCONJ
ijassa-899	380	2	ŵj	ŵj	PROPN
ijassa-899	380	3	,	,	PUNCT
ijassa-899	380	4	θ̂j	θ̂j	PROPN
ijassa-899	380	5	are	be	AUX
ijassa-899	380	6	estimations	estimation	NOUN
ijassa-899	380	7	of	of	ADP
ijassa-899	380	8	the	the	DET
ijassa-899	380	9	parameters	parameter	NOUN
ijassa-899	380	10	wj	wj	PROPN
ijassa-899	380	11	,	,	PUNCT
ijassa-899	380	12	θj	θj	ADV
ijassa-899	380	13	respectively	respectively	ADV
ijassa-899	380	14	,	,	PUNCT
ijassa-899	380	15	then	then	ADV
ijassa-899	380	16	we	we	PRON
ijassa-899	380	17	can	can	AUX
ijassa-899	380	18	estimate	estimate	VERB
ijassa-899	380	19	p̂j(x	p̂j(x	PROPN
ijassa-899	380	20	)	)	PUNCT
ijassa-899	380	21	=	=	SYM
ijassa-899	380	22	ŵjp(x|θ̂j	ŵjp(x|θ̂j	NOUN
ijassa-899	380	23	)	)	PUNCT
ijassa-899	380	24	/	/	SYM
ijassa-899	381	1	k∑	k∑	PROPN
ijassa-899	382	1	s=1	s=1	INTJ
ijassa-899	382	2	ŵsp(x|θ̂s	ŵsp(x|θ̂s	PROPN
ijassa-899	382	3	)	)	PUNCT
ijassa-899	382	4	.	.	PUNCT
ijassa-899	383	1	these	these	DET
ijassa-899	383	2	probability	probability	NOUN
ijassa-899	383	3	estimation	estimation	NOUN
ijassa-899	383	4	can	can	AUX
ijassa-899	383	5	be	be	AUX
ijassa-899	383	6	considered	consider	VERB
ijassa-899	383	7	as	as	ADP
ijassa-899	383	8	the	the	DET
ijassa-899	383	9	confidence	confidence	NOUN
ijassa-899	383	10	level	level	NOUN
ijassa-899	383	11	of	of	ADP
ijassa-899	383	12	the	the	DET
ijassa-899	383	13	method	method	NOUN
ijassa-899	383	14	when	when	SCONJ
ijassa-899	383	15	assigning	assign	VERB
ijassa-899	383	16	an	an	DET
ijassa-899	383	17	object	object	NOUN
ijassa-899	383	18	x	x	PUNCT
ijassa-899	383	19	to	to	ADP
ijassa-899	383	20	a	a	DET
ijassa-899	383	21	cluster	cluster	NOUN
ijassa-899	383	22	j	j	NOUN
ijassa-899	383	23	,	,	PUNCT
ijassa-899	383	24	which	which	PRON
ijassa-899	383	25	is	be	AUX
ijassa-899	383	26	sufficient	sufficient	ADJ
ijassa-899	383	27	to	to	PART
ijassa-899	383	28	solve	solve	VERB
ijassa-899	383	29	the	the	DET
ijassa-899	383	30	following	following	ADJ
ijassa-899	383	31	problem	problem	NOUN
ijassa-899	383	32	.	.	PUNCT
ijassa-899	384	1	performing	perform	VERB
ijassa-899	384	2	”	"	PUNCT
ijassa-899	384	3	hard	hard	ADJ
ijassa-899	384	4	”	"	PUNCT
ijassa-899	384	5	clustering	clustering	NOUN
ijassa-899	384	6	,	,	PUNCT
ijassa-899	384	7	where	where	SCONJ
ijassa-899	384	8	an	an	DET
ijassa-899	384	9	object	object	NOUN
ijassa-899	384	10	x	x	PUNCT
ijassa-899	384	11	must	must	AUX
ijassa-899	384	12	be	be	AUX
ijassa-899	384	13	strictly	strictly	ADV
ijassa-899	384	14	attributed	attribute	VERB
ijassa-899	384	15	to	to	ADP
ijassa-899	384	16	one	one	NUM
ijassa-899	384	17	of	of	ADP
ijassa-899	384	18	the	the	DET
ijassa-899	384	19	clusters	cluster	NOUN
ijassa-899	384	20	,	,	PUNCT
ijassa-899	384	21	the	the	DET
ijassa-899	384	22	cluster	cluster	NOUN
ijassa-899	384	23	with	with	ADP
ijassa-899	384	24	the	the	DET
ijassa-899	384	25	maximum	maximum	ADJ
ijassa-899	384	26	probability	probability	NOUN
ijassa-899	384	27	p̂j(x	p̂j(x	PROPN
ijassa-899	384	28	)	)	PUNCT
ijassa-899	384	29	is	be	AUX
ijassa-899	384	30	selected	select	VERB
ijassa-899	384	31	j∗	j∗	NOUN
ijassa-899	384	32	=	=	SYM
ijassa-899	384	33	arg	arg	NOUN
ijassa-899	384	34	max	max	PROPN
ijassa-899	384	35	j	j	PROPN
ijassa-899	384	36	p̂j(x	p̂j(x	PROPN
ijassa-899	384	37	)	)	PUNCT
ijassa-899	384	38	=	=	PUNCT
ijassa-899	384	39	arg	arg	NOUN
ijassa-899	384	40	max	max	PROPN
ijassa-899	384	41	j	j	PROPN
ijassa-899	384	42	ŵjp(x|θ̂j	ŵjp(x|θ̂j	PROPN
ijassa-899	384	43	)	)	PUNCT
ijassa-899	384	44	.	.	PUNCT
ijassa-899	385	1	5.2	5.2	NUM
ijassa-899	385	2	.	.	PUNCT
ijassa-899	386	1	anomalies	anomaly	NOUN
ijassa-899	386	2	object	object	VERB
ijassa-899	386	3	x	x	SYM
ijassa-899	386	4	∈	∈	PROPN
ijassa-899	386	5	rd	rd	NOUN
ijassa-899	386	6	is	be	AUX
ijassa-899	386	7	considered	consider	VERB
ijassa-899	386	8	abnormal	abnormal	ADJ
ijassa-899	386	9	if	if	SCONJ
ijassa-899	386	10	density	density	NOUN
ijassa-899	386	11	value	value	NOUN
ijassa-899	386	12	p(x	p(x	NOUN
ijassa-899	386	13	)	)	PUNCT
ijassa-899	386	14	is	be	AUX
ijassa-899	386	15	less	less	ADJ
ijassa-899	386	16	than	than	SCONJ
ijassa-899	386	17	some	some	DET
ijassa-899	386	18	threshold	threshold	NOUN
ijassa-899	386	19	value	value	NOUN
ijassa-899	386	20	q.	q.	NOUN
ijassa-899	387	1	the	the	DET
ijassa-899	387	2	value	value	NOUN
ijassa-899	387	3	q	q	NOUN
ijassa-899	387	4	is	be	AUX
ijassa-899	387	5	chosen	choose	VERB
ijassa-899	387	6	as	as	ADP
ijassa-899	387	7	density	density	NOUN
ijassa-899	387	8	value	value	NOUN
ijassa-899	387	9	p(x	p(x	NOUN
ijassa-899	387	10	)	)	PUNCT
ijassa-899	387	11	so	so	SCONJ
ijassa-899	387	12	that	that	SCONJ
ijassa-899	387	13	the	the	DET
ijassa-899	387	14	probability	probability	NOUN
ijassa-899	387	15	of	of	ADP
ijassa-899	387	16	getting	get	VERB
ijassa-899	387	17	an	an	DET
ijassa-899	387	18	object	object	NOUN
ijassa-899	387	19	with	with	ADP
ijassa-899	387	20	copyright	copyright	NOUN
ijassa-899	387	21	©	©	PROPN
ijassa-899	387	22	2020	2020	NUM
ijassa-899	387	23	assa	assa	NOUN
ijassa-899	387	24	.	.	PUNCT
ijassa-899	388	1	adv	adv	PROPN
ijassa-899	388	2	syst	syst	PROPN
ijassa-899	388	3	sci	sci	PROPN
ijassa-899	388	4	appl	appl	PROPN
ijassa-899	388	5	(	(	PUNCT
ijassa-899	388	6	2020	2020	NUM
ijassa-899	388	7	)	)	PUNCT
ijassa-899	388	8	110	110	NUM
ijassa-899	388	9	n.a	n.a	PROPN
ijassa-899	388	10	.	.	PROPN
ijassa-899	388	11	volkov	volkov	PROPN
ijassa-899	388	12	,	,	PUNCT
ijassa-899	388	13	e.yu	e.yu	PROPN
ijassa-899	388	14	.	.	PROPN
ijassa-899	388	15	dakhova	dakhova	PROPN
ijassa-899	388	16	,	,	PUNCT
ijassa-899	388	17	s.a	s.a	PROPN
ijassa-899	388	18	.	.	PROPN
ijassa-899	388	19	budennyy	budennyy	PROPN
ijassa-899	388	20	,	,	PUNCT
ijassa-899	388	21	a.m.	a.m.	PROPN
ijassa-899	388	22	andrianova	andrianova	VERB
ijassa-899	388	23	a	a	DET
ijassa-899	388	24	density	density	NOUN
ijassa-899	388	25	value	value	NOUN
ijassa-899	388	26	not	not	PART
ijassa-899	388	27	exceeding	exceed	VERB
ijassa-899	388	28	q	q	NOUN
ijassa-899	388	29	is	be	AUX
ijassa-899	388	30	exactly	exactly	ADV
ijassa-899	388	31	0.05	0.05	NUM
ijassa-899	388	32	.	.	PUNCT
ijassa-899	389	1	in	in	ADP
ijassa-899	389	2	other	other	ADJ
ijassa-899	389	3	words	word	NOUN
ijassa-899	389	4	,	,	PUNCT
ijassa-899	389	5	the	the	DET
ijassa-899	389	6	value	value	NOUN
ijassa-899	389	7	q	q	NOUN
ijassa-899	389	8	is	be	AUX
ijassa-899	389	9	the	the	DET
ijassa-899	389	10	solution	solution	NOUN
ijassa-899	389	11	of	of	ADP
ijassa-899	389	12	the	the	DET
ijassa-899	389	13	equation	equation	NOUN
ijassa-899	389	14	∫	∫	PROPN
ijassa-899	389	15	rd	rd	PROPN
ijassa-899	389	16	p(x)i{p(x	p(x)i{p(x	PROPN
ijassa-899	389	17	)	)	PUNCT
ijassa-899	389	18	6	6	NUM
ijassa-899	389	19	q}dx	q}dx	PROPN
ijassa-899	389	20	=	=	NOUN
ijassa-899	389	21	0.05	0.05	NUM
ijassa-899	389	22	.	.	PUNCT
ijassa-899	390	1	the	the	DET
ijassa-899	390	2	described	describe	VERB
ijassa-899	390	3	procedure	procedure	NOUN
ijassa-899	390	4	for	for	ADP
ijassa-899	390	5	determining	determine	VERB
ijassa-899	390	6	anomalous	anomalous	ADJ
ijassa-899	390	7	objects	object	NOUN
ijassa-899	390	8	is	be	AUX
ijassa-899	390	9	a	a	DET
ijassa-899	390	10	special	special	ADJ
ijassa-899	390	11	case	case	NOUN
ijassa-899	390	12	of	of	ADP
ijassa-899	390	13	the	the	DET
ijassa-899	390	14	procedure	procedure	NOUN
ijassa-899	390	15	for	for	ADP
ijassa-899	390	16	checking	check	VERB
ijassa-899	390	17	statistical	statistical	ADJ
ijassa-899	390	18	hypotheses	hypothesis	NOUN
ijassa-899	390	19	.	.	PUNCT
ijassa-899	391	1	in	in	ADP
ijassa-899	391	2	that	that	DET
ijassa-899	391	3	case	case	NOUN
ijassa-899	391	4	,	,	PUNCT
ijassa-899	391	5	the	the	DET
ijassa-899	391	6	hypothesis	hypothesis	NOUN
ijassa-899	391	7	that	that	PRON
ijassa-899	391	8	object	object	VERB
ijassa-899	391	9	x	x	PUNCT
ijassa-899	391	10	is	be	AUX
ijassa-899	391	11	typical	typical	ADJ
ijassa-899	391	12	is	be	AUX
ijassa-899	391	13	tested	test	VERB
ijassa-899	391	14	against	against	ADP
ijassa-899	391	15	the	the	DET
ijassa-899	391	16	alternative	alternative	ADJ
ijassa-899	391	17	one	one	NUM
ijassa-899	391	18	about	about	ADP
ijassa-899	391	19	object	object	NOUN
ijassa-899	391	20	abnormality	abnormality	NOUN
ijassa-899	391	21	.	.	PUNCT
ijassa-899	392	1	if	if	SCONJ
ijassa-899	392	2	the	the	DET
ijassa-899	392	3	density	density	NOUN
ijassa-899	392	4	at	at	ADP
ijassa-899	392	5	the	the	DET
ijassa-899	392	6	point	point	NOUN
ijassa-899	392	7	x	x	PUNCT
ijassa-899	392	8	is	be	AUX
ijassa-899	392	9	less	less	ADJ
ijassa-899	392	10	than	than	ADP
ijassa-899	392	11	the	the	DET
ijassa-899	392	12	threshold	threshold	NOUN
ijassa-899	392	13	value	value	NOUN
ijassa-899	392	14	q	q	NOUN
ijassa-899	392	15	,	,	PUNCT
ijassa-899	392	16	the	the	DET
ijassa-899	392	17	hypothesis	hypothesis	NOUN
ijassa-899	392	18	of	of	ADP
ijassa-899	392	19	object	object	NOUN
ijassa-899	392	20	typicity	typicity	NOUN
ijassa-899	392	21	is	be	AUX
ijassa-899	392	22	rejected	reject	VERB
ijassa-899	392	23	in	in	ADP
ijassa-899	392	24	favor	favor	NOUN
ijassa-899	392	25	of	of	ADP
ijassa-899	392	26	an	an	DET
ijassa-899	392	27	alternative	alternative	ADJ
ijassa-899	392	28	one	one	NUM
ijassa-899	392	29	at	at	ADP
ijassa-899	392	30	the	the	DET
ijassa-899	392	31	significance	significance	NOUN
ijassa-899	392	32	level	level	NOUN
ijassa-899	392	33	0.05	0.05	NUM
ijassa-899	392	34	.	.	PUNCT
ijassa-899	393	1	this	this	DET
ijassa-899	393	2	rule	rule	NOUN
ijassa-899	393	3	is	be	AUX
ijassa-899	393	4	a	a	DET
ijassa-899	393	5	criterion	criterion	NOUN
ijassa-899	393	6	for	for	ADP
ijassa-899	393	7	testing	test	VERB
ijassa-899	393	8	a	a	DET
ijassa-899	393	9	hypothesis	hypothesis	NOUN
ijassa-899	393	10	.	.	PUNCT
ijassa-899	394	1	a	a	DET
ijassa-899	394	2	probability	probability	NOUN
ijassa-899	394	3	of	of	ADP
ijassa-899	394	4	getting	get	VERB
ijassa-899	394	5	objects	object	NOUN
ijassa-899	394	6	with	with	ADP
ijassa-899	394	7	a	a	DET
ijassa-899	394	8	density	density	NOUN
ijassa-899	394	9	at	at	ADP
ijassa-899	394	10	least	least	ADJ
ijassa-899	394	11	p(x	p(x	PROPN
ijassa-899	394	12	)	)	PUNCT
ijassa-899	394	13	can	can	AUX
ijassa-899	394	14	be	be	AUX
ijassa-899	394	15	considered	consider	VERB
ijassa-899	394	16	as	as	ADP
ijassa-899	394	17	the	the	DET
ijassa-899	394	18	level	level	NOUN
ijassa-899	394	19	of	of	ADP
ijassa-899	394	20	typicity	typicity	NOUN
ijassa-899	394	21	of	of	ADP
ijassa-899	394	22	object	object	NOUN
ijassa-899	394	23	x.	x.	NOUN
ijassa-899	395	1	this	this	DET
ijassa-899	395	2	value	value	NOUN
ijassa-899	395	3	is	be	AUX
ijassa-899	395	4	analogous	analogous	ADJ
ijassa-899	395	5	to	to	ADP
ijassa-899	395	6	p	p	NOUN
ijassa-899	395	7	-	-	PUNCT
ijassa-899	395	8	value	value	NOUN
ijassa-899	395	9	and	and	CCONJ
ijassa-899	395	10	is	be	AUX
ijassa-899	395	11	computed	compute	VERB
ijassa-899	395	12	using	use	VERB
ijassa-899	395	13	the	the	DET
ijassa-899	395	14	integral∫	integral∫	NOUN
ijassa-899	395	15	rd	rd	NOUN
ijassa-899	395	16	p(y)i{p(y	p(y)i{p(y	PROPN
ijassa-899	395	17	)	)	PUNCT
ijassa-899	395	18	6	6	NUM
ijassa-899	395	19	p(x)}dy	p(x)}dy	NOUN
ijassa-899	395	20	.	.	PUNCT
ijassa-899	396	1	5.3	5.3	NUM
ijassa-899	396	2	.	.	PUNCT
ijassa-899	397	1	missing	miss	VERB
ijassa-899	397	2	data	datum	NOUN
ijassa-899	397	3	both	both	DET
ijassa-899	397	4	methods	method	NOUN
ijassa-899	397	5	discussed	discuss	VERB
ijassa-899	397	6	above	above	ADP
ijassa-899	397	7	work	work	NOUN
ijassa-899	397	8	only	only	ADV
ijassa-899	397	9	if	if	SCONJ
ijassa-899	397	10	all	all	DET
ijassa-899	397	11	values	value	NOUN
ijassa-899	397	12	of	of	ADP
ijassa-899	397	13	components	component	NOUN
ijassa-899	397	14	x	x	SYM
ijassa-899	397	15	∈	∈	PROPN
ijassa-899	397	16	rd	rd	NOUN
ijassa-899	397	17	are	be	AUX
ijassa-899	397	18	known	know	VERB
ijassa-899	397	19	,	,	PUNCT
ijassa-899	397	20	i.e.	i.e.	X
ijassa-899	397	21	there	there	PRON
ijassa-899	397	22	are	be	VERB
ijassa-899	397	23	no	no	DET
ijassa-899	397	24	missing	miss	VERB
ijassa-899	397	25	values	value	NOUN
ijassa-899	397	26	.	.	PUNCT
ijassa-899	398	1	otherwise	otherwise	ADV
ijassa-899	398	2	,	,	PUNCT
ijassa-899	398	3	the	the	DET
ijassa-899	398	4	density	density	NOUN
ijassa-899	398	5	of	of	ADP
ijassa-899	398	6	the	the	DET
ijassa-899	398	7	object	object	NOUN
ijassa-899	398	8	x	x	PUNCT
ijassa-899	398	9	can	can	AUX
ijassa-899	398	10	be	be	AUX
ijassa-899	398	11	estimated	estimate	VERB
ijassa-899	398	12	as	as	ADP
ijassa-899	398	13	the	the	DET
ijassa-899	398	14	density	density	NOUN
ijassa-899	398	15	integral	integral	ADJ
ijassa-899	398	16	over	over	ADP
ijassa-899	398	17	a	a	DET
ijassa-899	398	18	subspace	subspace	NOUN
ijassa-899	398	19	of	of	ADP
ijassa-899	398	20	omitted	omit	VERB
ijassa-899	398	21	values	value	NOUN
ijassa-899	398	22	.	.	PUNCT
ijassa-899	399	1	formally	formally	ADV
ijassa-899	399	2	,	,	PUNCT
ijassa-899	399	3	let	let	VERB
ijassa-899	399	4	xk	xk	PROPN
ijassa-899	399	5	be	be	AUX
ijassa-899	399	6	a	a	DET
ijassa-899	399	7	vector	vector	NOUN
ijassa-899	399	8	of	of	ADP
ijassa-899	399	9	known	know	VERB
ijassa-899	399	10	object	object	NOUN
ijassa-899	399	11	values	value	NOUN
ijassa-899	399	12	,	,	PUNCT
ijassa-899	399	13	and	and	CCONJ
ijassa-899	399	14	xu	xu	PROPN
ijassa-899	399	15	are	be	AUX
ijassa-899	399	16	all	all	PRON
ijassa-899	399	17	other	other	ADJ
ijassa-899	399	18	object	object	NOUN
ijassa-899	399	19	values	value	NOUN
ijassa-899	399	20	that	that	PRON
ijassa-899	399	21	are	be	AUX
ijassa-899	399	22	omitted	omit	VERB
ijassa-899	399	23	.	.	PUNCT
ijassa-899	400	1	then∫	then∫	PROPN
ijassa-899	400	2	rdu	rdu	PROPN
ijassa-899	400	3	p(x)dxu	p(x)dxu	PROPN
ijassa-899	400	4	,	,	PUNCT
ijassa-899	400	5	where	where	SCONJ
ijassa-899	400	6	du	du	PROPN
ijassa-899	400	7	is	be	AUX
ijassa-899	400	8	a	a	DET
ijassa-899	400	9	dimension	dimension	NOUN
ijassa-899	400	10	of	of	ADP
ijassa-899	400	11	vector	vector	NOUN
ijassa-899	400	12	xu	xu	PROPN
ijassa-899	400	13	.	.	PUNCT
ijassa-899	401	1	for	for	ADP
ijassa-899	401	2	a	a	DET
ijassa-899	401	3	normal	normal	ADJ
ijassa-899	401	4	or	or	CCONJ
ijassa-899	401	5	student	student	NOUN
ijassa-899	401	6	distribution	distribution	NOUN
ijassa-899	401	7	mixture	mixture	NOUN
ijassa-899	401	8	,	,	PUNCT
ijassa-899	401	9	the	the	DET
ijassa-899	401	10	density	density	NOUN
ijassa-899	401	11	is	be	AUX
ijassa-899	401	12	equal	equal	ADJ
ijassa-899	401	13	to	to	ADP
ijassa-899	401	14	the	the	DET
ijassa-899	401	15	mixture	mixture	NOUN
ijassa-899	401	16	of	of	ADP
ijassa-899	401	17	marginal	marginal	ADJ
ijassa-899	401	18	distributions	distribution	NOUN
ijassa-899	401	19	obtained	obtain	VERB
ijassa-899	401	20	in	in	ADP
ijassa-899	401	21	claims	claim	NOUN
ijassa-899	401	22	2.2	2.2	NUM
ijassa-899	401	23	.	.	NOUN
ijassa-899	402	1	5.4	5.4	NUM
ijassa-899	402	2	.	.	PUNCT
ijassa-899	403	1	conditional	conditional	ADJ
ijassa-899	403	2	distribution	distribution	NOUN
ijassa-899	403	3	and	and	CCONJ
ijassa-899	403	4	probabilistic	probabilistic	ADJ
ijassa-899	403	5	regression	regression	NOUN
ijassa-899	403	6	on	on	ADP
ijassa-899	403	7	features	feature	NOUN
ijassa-899	403	8	let	let	VERB
ijassa-899	403	9	object	object	VERB
ijassa-899	403	10	x	x	SYM
ijassa-899	403	11	∈	∈	PROPN
ijassa-899	403	12	rd	rd	NOUN
ijassa-899	403	13	be	be	AUX
ijassa-899	403	14	recognized	recognize	VERB
ijassa-899	403	15	as	as	ADP
ijassa-899	403	16	anomalous	anomalous	ADJ
ijassa-899	403	17	.	.	PUNCT
ijassa-899	404	1	let	let	VERB
ijassa-899	404	2	us	we	PRON
ijassa-899	404	3	select	select	VERB
ijassa-899	404	4	elements	element	NOUN
ijassa-899	404	5	of	of	ADP
ijassa-899	404	6	the	the	DET
ijassa-899	404	7	vector	vector	NOUN
ijassa-899	404	8	x	x	PART
ijassa-899	404	9	to	to	PART
ijassa-899	404	10	trust	trust	VERB
ijassa-899	404	11	in	in	ADP
ijassa-899	404	12	and	and	CCONJ
ijassa-899	404	13	evaluate	evaluate	VERB
ijassa-899	404	14	others	other	NOUN
ijassa-899	404	15	through	through	ADP
ijassa-899	404	16	them	they	PRON
ijassa-899	404	17	.	.	PUNCT
ijassa-899	405	1	without	without	ADP
ijassa-899	405	2	loss	loss	NOUN
ijassa-899	405	3	of	of	ADP
ijassa-899	405	4	generality	generality	NOUN
ijassa-899	405	5	we	we	PRON
ijassa-899	405	6	assume	assume	VERB
ijassa-899	405	7	that	that	SCONJ
ijassa-899	405	8	xt	xt	PROPN
ijassa-899	405	9	=	=	SYM
ijassa-899	405	10	(	(	PUNCT
ijassa-899	405	11	xta	xta	PROPN
ijassa-899	405	12	,	,	PUNCT
ijassa-899	406	1	x	x	PROPN
ijassa-899	406	2	t	t	PROPN
ijassa-899	406	3	b	b	PROPN
ijassa-899	406	4	)	)	PUNCT
ijassa-899	406	5	and	and	CCONJ
ijassa-899	406	6	the	the	DET
ijassa-899	406	7	values	value	NOUN
ijassa-899	406	8	of	of	ADP
ijassa-899	406	9	xb	xb	PROPN
ijassa-899	406	10	are	be	AUX
ijassa-899	406	11	trusted	trust	VERB
ijassa-899	406	12	.	.	PUNCT
ijassa-899	407	1	in	in	ADP
ijassa-899	407	2	addition	addition	NOUN
ijassa-899	407	3	,	,	PUNCT
ijassa-899	407	4	due	due	ADP
ijassa-899	407	5	to	to	ADP
ijassa-899	407	6	missing	miss	VERB
ijassa-899	407	7	data	datum	NOUN
ijassa-899	407	8	,	,	PUNCT
ijassa-899	407	9	some	some	DET
ijassa-899	407	10	values	value	NOUN
ijassa-899	407	11	of	of	ADP
ijassa-899	407	12	xa	xa	PROPN
ijassa-899	407	13	may	may	AUX
ijassa-899	407	14	not	not	PART
ijassa-899	407	15	be	be	AUX
ijassa-899	407	16	known	know	VERB
ijassa-899	407	17	.	.	PUNCT
ijassa-899	408	1	according	accord	VERB
ijassa-899	408	2	to	to	ADP
ijassa-899	408	3	claim	claim	NOUN
ijassa-899	408	4	3.2	3.2	NUM
ijassa-899	408	5	,	,	PUNCT
ijassa-899	408	6	vector	vector	NOUN
ijassa-899	408	7	xa	xa	PROPN
ijassa-899	408	8	under	under	ADP
ijassa-899	408	9	the	the	DET
ijassa-899	408	10	condition	condition	NOUN
ijassa-899	408	11	of	of	ADP
ijassa-899	408	12	values	value	NOUN
ijassa-899	408	13	xb	xb	PROPN
ijassa-899	408	14	has	have	VERB
ijassa-899	408	15	density	density	NOUN
ijassa-899	408	16	p̃(x	p̃(x	NOUN
ijassa-899	408	17	)	)	PUNCT
ijassa-899	408	18	=	=	PUNCT
ijassa-899	409	1	k∑	k∑	PROPN
ijassa-899	410	1	j=1	j=1	PROPN
ijassa-899	410	2	w̃jp(x|θ̃j	w̃jp(x|θ̃j	PROPN
ijassa-899	410	3	)	)	PUNCT
ijassa-899	410	4	,	,	PUNCT
ijassa-899	410	5	where	where	SCONJ
ijassa-899	410	6	θ̃j	θ̃j	PROPN
ijassa-899	410	7	is	be	AUX
ijassa-899	410	8	the	the	DET
ijassa-899	410	9	parameter	parameter	NOUN
ijassa-899	410	10	of	of	ADP
ijassa-899	410	11	the	the	DET
ijassa-899	410	12	mixture	mixture	NOUN
ijassa-899	410	13	distribution	distribution	NOUN
ijassa-899	410	14	jth	jth	PROPN
ijassa-899	410	15	component	component	NOUN
ijassa-899	410	16	given	give	VERB
ijassa-899	410	17	xb	xb	PROPN
ijassa-899	410	18	.	.	PUNCT
ijassa-899	411	1	in	in	ADP
ijassa-899	411	2	the	the	DET
ijassa-899	411	3	case	case	NOUN
ijassa-899	411	4	of	of	ADP
ijassa-899	411	5	the	the	DET
ijassa-899	411	6	normal	normal	ADJ
ijassa-899	411	7	mixture	mixture	NOUN
ijassa-899	411	8	,	,	PUNCT
ijassa-899	411	9	the	the	DET
ijassa-899	411	10	parameters	parameter	NOUN
ijassa-899	411	11	θ̃j	θ̃j	VERB
ijassa-899	411	12	for	for	ADP
ijassa-899	411	13	each	each	DET
ijassa-899	411	14	cluster	cluster	NOUN
ijassa-899	411	15	are	be	AUX
ijassa-899	411	16	computed	compute	VERB
ijassa-899	411	17	in	in	ADP
ijassa-899	411	18	accordance	accordance	NOUN
ijassa-899	411	19	with	with	ADP
ijassa-899	411	20	relations	relation	NOUN
ijassa-899	411	21	from	from	ADP
ijassa-899	411	22	claim	claim	NOUN
ijassa-899	411	23	2.3	2.3	NUM
ijassa-899	411	24	.	.	PUNCT
ijassa-899	412	1	in	in	ADP
ijassa-899	412	2	the	the	DET
ijassa-899	412	3	case	case	NOUN
ijassa-899	412	4	of	of	ADP
ijassa-899	412	5	the	the	DET
ijassa-899	412	6	student	student	NOUN
ijassa-899	412	7	mixture	mixture	NOUN
ijassa-899	412	8	,	,	PUNCT
ijassa-899	412	9	the	the	DET
ijassa-899	412	10	parameters	parameter	NOUN
ijassa-899	412	11	are	be	AUX
ijassa-899	412	12	computed	compute	VERB
ijassa-899	412	13	in	in	ADP
ijassa-899	412	14	accordance	accordance	NOUN
ijassa-899	412	15	with	with	ADP
ijassa-899	412	16	relations	relation	NOUN
ijassa-899	412	17	from	from	ADP
ijassa-899	412	18	the	the	DET
ijassa-899	412	19	theorem	theorem	ADJ
ijassa-899	412	20	2.1	2.1	NUM
ijassa-899	412	21	.	.	PUNCT
ijassa-899	412	22	replacing	replace	VERB
ijassa-899	412	23	the	the	DET
ijassa-899	412	24	parameters	parameter	NOUN
ijassa-899	412	25	with	with	ADP
ijassa-899	412	26	their	their	PRON
ijassa-899	412	27	estimations	estimation	NOUN
ijassa-899	412	28	,	,	PUNCT
ijassa-899	412	29	we	we	PRON
ijassa-899	412	30	get	get	VERB
ijassa-899	412	31	an	an	DET
ijassa-899	412	32	estimation	estimation	NOUN
ijassa-899	412	33	of	of	ADP
ijassa-899	412	34	the	the	DET
ijassa-899	412	35	conditional	conditional	ADJ
ijassa-899	412	36	distribution	distribution	NOUN
ijassa-899	412	37	of	of	ADP
ijassa-899	412	38	vector	vector	PROPN
ijassa-899	412	39	xa	xa	PROPN
ijassa-899	412	40	,	,	PUNCT
ijassa-899	412	41	which	which	PRON
ijassa-899	412	42	is	be	AUX
ijassa-899	412	43	sufficiently	sufficiently	ADV
ijassa-899	412	44	informative	informative	ADJ
ijassa-899	412	45	for	for	ADP
ijassa-899	412	46	making	make	VERB
ijassa-899	412	47	various	various	ADJ
ijassa-899	412	48	conclusions	conclusion	NOUN
ijassa-899	412	49	about	about	ADP
ijassa-899	412	50	the	the	DET
ijassa-899	412	51	vector	vector	NOUN
ijassa-899	412	52	xa	xa	PROPN
ijassa-899	412	53	in	in	ADP
ijassa-899	412	54	practice	practice	NOUN
ijassa-899	412	55	.	.	PUNCT
ijassa-899	413	1	in	in	ADP
ijassa-899	413	2	particular	particular	ADJ
ijassa-899	413	3	,	,	PUNCT
ijassa-899	413	4	it	it	PRON
ijassa-899	413	5	can	can	AUX
ijassa-899	413	6	be	be	AUX
ijassa-899	413	7	used	use	VERB
ijassa-899	413	8	to	to	PART
ijassa-899	413	9	compute	compute	VERB
ijassa-899	413	10	•	•	NOUN
ijassa-899	413	11	expectation	expectation	NOUN
ijassa-899	413	12	value	value	NOUN
ijassa-899	413	13	e(xa	e(xa	NOUN
ijassa-899	413	14	|	|	NOUN
ijassa-899	413	15	xb	xb	PROPN
ijassa-899	413	16	)	)	PUNCT
ijassa-899	413	17	according	accord	VERB
ijassa-899	413	18	to	to	ADP
ijassa-899	413	19	claim	claim	NOUN
ijassa-899	413	20	3.1	3.1	NUM
ijassa-899	413	21	.	.	PUNCT
ijassa-899	414	1	this	this	DET
ijassa-899	414	2	estimation	estimation	NOUN
ijassa-899	414	3	solves	solve	VERB
ijassa-899	414	4	the	the	DET
ijassa-899	414	5	problem	problem	NOUN
ijassa-899	414	6	of	of	ADP
ijassa-899	414	7	regression	regression	NOUN
ijassa-899	414	8	of	of	ADP
ijassa-899	414	9	xb	xb	PROPN
ijassa-899	414	10	features	feature	NOUN
ijassa-899	414	11	to	to	ADP
ijassa-899	414	12	xa	xa	PROPN
ijassa-899	414	13	features	feature	NOUN
ijassa-899	414	14	.	.	PUNCT
ijassa-899	415	1	note	note	VERB
ijassa-899	415	2	that	that	SCONJ
ijassa-899	415	3	the	the	DET
ijassa-899	415	4	regression	regression	NOUN
ijassa-899	415	5	problem	problem	NOUN
ijassa-899	415	6	can	can	AUX
ijassa-899	415	7	be	be	AUX
ijassa-899	415	8	solved	solve	VERB
ijassa-899	415	9	for	for	ADP
ijassa-899	415	10	different	different	ADJ
ijassa-899	415	11	features	feature	NOUN
ijassa-899	415	12	xa	xa	PROPN
ijassa-899	415	13	and	and	CCONJ
ijassa-899	415	14	xb	xb	PROPN
ijassa-899	415	15	using	use	VERB
ijassa-899	415	16	only	only	ADV
ijassa-899	415	17	one	one	NUM
ijassa-899	415	18	model	model	NOUN
ijassa-899	415	19	.	.	PUNCT
ijassa-899	416	1	copyright	copyright	NOUN
ijassa-899	416	2	©	©	PROPN
ijassa-899	416	3	2020	2020	NUM
ijassa-899	416	4	assa	assa	NOUN
ijassa-899	416	5	.	.	PUNCT
ijassa-899	417	1	adv	adv	PROPN
ijassa-899	417	2	syst	syst	PROPN
ijassa-899	417	3	sci	sci	PROPN
ijassa-899	417	4	appl	appl	PROPN
ijassa-899	417	5	(	(	PUNCT
ijassa-899	417	6	2020	2020	NUM
ijassa-899	417	7	)	)	PUNCT
ijassa-899	417	8	student	student	NOUN
ijassa-899	417	9	mixture	mixture	NOUN
ijassa-899	417	10	and	and	CCONJ
ijassa-899	417	11	its	its	PRON
ijassa-899	417	12	machine	machine	NOUN
ijassa-899	417	13	learning	learn	VERB
ijassa-899	417	14	applications	application	NOUN
ijassa-899	417	15	to	to	ADP
ijassa-899	417	16	pvt	pvt	PROPN
ijassa-899	417	17	properties	property	NOUN
ijassa-899	417	18	111	111	NUM
ijassa-899	417	19	•	•	NOUN
ijassa-899	417	20	variance	variance	NOUN
ijassa-899	417	21	estimation	estimation	NOUN
ijassa-899	417	22	var(xa	var(xa	NOUN
ijassa-899	417	23	|	|	NOUN
ijassa-899	417	24	xb	xb	PROPN
ijassa-899	417	25	)	)	PUNCT
ijassa-899	417	26	according	accord	VERB
ijassa-899	417	27	to	to	ADP
ijassa-899	417	28	claim	claim	NOUN
ijassa-899	417	29	3.2	3.2	NUM
ijassa-899	417	30	.	.	NOUN
ijassa-899	418	1	•	•	NUM
ijassa-899	418	2	evaluation	evaluation	NOUN
ijassa-899	418	3	of	of	ADP
ijassa-899	418	4	conditional	conditional	ADJ
ijassa-899	418	5	cluster	cluster	NOUN
ijassa-899	418	6	distributions	distribution	NOUN
ijassa-899	418	7	for	for	ADP
ijassa-899	418	8	all	all	DET
ijassa-899	418	9	objects	object	NOUN
ijassa-899	418	10	that	that	PRON
ijassa-899	418	11	havexb	havexb	PROPN
ijassa-899	418	12	attributes	attribute	VERB
ijassa-899	418	13	fixed	fix	VERB
ijassa-899	418	14	and	and	CCONJ
ijassa-899	418	15	equal	equal	ADJ
ijassa-899	418	16	to	to	ADP
ijassa-899	418	17	xb	xb	PROPN
ijassa-899	418	18	.	.	PROPN
ijassa-899	419	1	•	•	NUM
ijassa-899	419	2	set	set	NOUN
ijassa-899	419	3	of	of	ADP
ijassa-899	419	4	the	the	DET
ijassa-899	419	5	highest	high	ADJ
ijassa-899	419	6	density	density	NOUN
ijassa-899	419	7	,	,	PUNCT
ijassa-899	419	8	i.e.	i.e.	X
ijassa-899	419	9	set	set	NOUN
ijassa-899	419	10	of	of	ADP
ijassa-899	419	11	values	value	NOUN
ijassa-899	419	12	xa	xa	PROPN
ijassa-899	419	13	,	,	PUNCT
ijassa-899	419	14	for	for	ADP
ijassa-899	419	15	which	which	PRON
ijassa-899	419	16	the	the	DET
ijassa-899	419	17	density	density	NOUN
ijassa-899	419	18	of	of	ADP
ijassa-899	419	19	the	the	DET
ijassa-899	419	20	conditional	conditional	ADJ
ijassa-899	419	21	distribution	distribution	NOUN
ijassa-899	419	22	is	be	AUX
ijassa-899	419	23	greater	great	ADJ
ijassa-899	419	24	than	than	ADP
ijassa-899	419	25	for	for	ADP
ijassa-899	419	26	the	the	DET
ijassa-899	419	27	other	other	ADJ
ijassa-899	419	28	values	value	NOUN
ijassa-899	419	29	.	.	PUNCT
ijassa-899	420	1	such	such	DET
ijassa-899	420	2	a	a	DET
ijassa-899	420	3	set	set	NOUN
ijassa-899	420	4	is	be	AUX
ijassa-899	420	5	analogous	analogous	ADJ
ijassa-899	420	6	to	to	ADP
ijassa-899	420	7	the	the	DET
ijassa-899	420	8	confidence	confidence	NOUN
ijassa-899	420	9	domain	domain	NOUN
ijassa-899	420	10	of	of	ADP
ijassa-899	420	11	the	the	DET
ijassa-899	420	12	minimum	minimum	ADJ
ijassa-899	420	13	volume	volume	NOUN
ijassa-899	420	14	.	.	PUNCT
ijassa-899	421	1	in	in	ADP
ijassa-899	421	2	the	the	DET
ijassa-899	421	3	one	one	NUM
ijassa-899	421	4	-	-	PUNCT
ijassa-899	421	5	dimensional	dimensional	ADJ
ijassa-899	421	6	case	case	NOUN
ijassa-899	421	7	,	,	PUNCT
ijassa-899	421	8	this	this	DET
ijassa-899	421	9	set	set	NOUN
ijassa-899	421	10	is	be	AUX
ijassa-899	421	11	an	an	DET
ijassa-899	421	12	interval	interval	NOUN
ijassa-899	421	13	or	or	CCONJ
ijassa-899	421	14	set	set	NOUN
ijassa-899	421	15	of	of	ADP
ijassa-899	421	16	intervals	interval	NOUN
ijassa-899	421	17	.	.	PUNCT
ijassa-899	422	1	6	6	X
ijassa-899	422	2	.	.	X
ijassa-899	422	3	modeling	model	VERB
ijassa-899	422	4	pvt	pvt	PROPN
ijassa-899	422	5	properties	property	NOUN
ijassa-899	422	6	of	of	ADP
ijassa-899	422	7	reservoir	reservoir	NOUN
ijassa-899	422	8	fluids	fluid	NOUN
ijassa-899	422	9	using	use	VERB
ijassa-899	422	10	a	a	DET
ijassa-899	422	11	probabilistic	probabilistic	ADJ
ijassa-899	422	12	model	model	NOUN
ijassa-899	422	13	6.1	6.1	NUM
ijassa-899	422	14	.	.	PUNCT
ijassa-899	423	1	data	datum	NOUN
ijassa-899	423	2	description	description	NOUN
ijassa-899	423	3	there	there	PRON
ijassa-899	423	4	are	be	VERB
ijassa-899	423	5	several	several	ADJ
ijassa-899	423	6	of	of	ADP
ijassa-899	423	7	methods	method	NOUN
ijassa-899	423	8	for	for	ADP
ijassa-899	423	9	evaluating	evaluate	VERB
ijassa-899	423	10	the	the	DET
ijassa-899	423	11	representativeness	representativeness	NOUN
ijassa-899	423	12	of	of	ADP
ijassa-899	423	13	reservoir	reservoir	NOUN
ijassa-899	423	14	fluid	fluid	NOUN
ijassa-899	423	15	samples	sample	NOUN
ijassa-899	423	16	[	[	X
ijassa-899	423	17	15	15	NUM
ijassa-899	423	18	]	]	X
ijassa-899	423	19	,	,	PUNCT
ijassa-899	423	20	such	such	ADJ
ijassa-899	423	21	as	as	ADP
ijassa-899	423	22	checking	check	VERB
ijassa-899	423	23	the	the	DET
ijassa-899	423	24	tightness	tightness	NOUN
ijassa-899	423	25	of	of	ADP
ijassa-899	423	26	sampling	sample	VERB
ijassa-899	423	27	chambers	chamber	NOUN
ijassa-899	423	28	,	,	PUNCT
ijassa-899	423	29	comparing	compare	VERB
ijassa-899	423	30	the	the	DET
ijassa-899	423	31	oil	oil	NOUN
ijassa-899	423	32	saturation	saturation	NOUN
ijassa-899	423	33	pressure	pressure	NOUN
ijassa-899	423	34	with	with	ADP
ijassa-899	423	35	the	the	DET
ijassa-899	423	36	separation	separation	NOUN
ijassa-899	423	37	pressure	pressure	NOUN
ijassa-899	423	38	at	at	ADP
ijassa-899	423	39	the	the	DET
ijassa-899	423	40	separation	separation	NOUN
ijassa-899	423	41	temperature	temperature	NOUN
ijassa-899	423	42	,	,	PUNCT
ijassa-899	423	43	etc	etc	X
ijassa-899	423	44	.	.	X
ijassa-899	423	45	;	;	PUNCT
ijassa-899	423	46	the	the	DET
ijassa-899	423	47	hoffman	hoffman	PROPN
ijassa-899	423	48	-	-	PUNCT
ijassa-899	423	49	kramp	kramp	PROPN
ijassa-899	423	50	-	-	PUNCT
ijassa-899	423	51	hockot	hockot	PROPN
ijassa-899	423	52	method	method	NOUN
ijassa-899	423	53	,	,	PUNCT
ijassa-899	423	54	based	base	VERB
ijassa-899	423	55	on	on	ADP
ijassa-899	423	56	the	the	DET
ijassa-899	423	57	correlation	correlation	NOUN
ijassa-899	423	58	of	of	ADP
ijassa-899	423	59	equilibrium	equilibrium	NOUN
ijassa-899	423	60	constants	constant	NOUN
ijassa-899	423	61	;	;	PUNCT
ijassa-899	423	62	determining	determine	VERB
ijassa-899	423	63	the	the	DET
ijassa-899	423	64	representativeness	representativeness	NOUN
ijassa-899	423	65	of	of	ADP
ijassa-899	423	66	samples	sample	NOUN
ijassa-899	423	67	by	by	ADP
ijassa-899	423	68	the	the	DET
ijassa-899	423	69	criterion	criterion	NOUN
ijassa-899	423	70	of	of	ADP
ijassa-899	423	71	contamination	contamination	NOUN
ijassa-899	423	72	with	with	ADP
ijassa-899	423	73	process	process	NOUN
ijassa-899	423	74	fluids	fluid	NOUN
ijassa-899	423	75	used	use	VERB
ijassa-899	423	76	in	in	ADP
ijassa-899	423	77	drilling	drilling	NOUN
ijassa-899	423	78	,	,	PUNCT
ijassa-899	423	79	perforation	perforation	NOUN
ijassa-899	423	80	and	and	CCONJ
ijassa-899	423	81	development	development	NOUN
ijassa-899	423	82	of	of	ADP
ijassa-899	423	83	wells	wells	PROPN
ijassa-899	423	84	.	.	PUNCT
ijassa-899	424	1	in	in	ADP
ijassa-899	424	2	conditions	condition	NOUN
ijassa-899	424	3	where	where	SCONJ
ijassa-899	424	4	only	only	ADV
ijassa-899	424	5	raw	raw	ADJ
ijassa-899	424	6	data	datum	NOUN
ijassa-899	424	7	is	be	AUX
ijassa-899	424	8	available	available	ADJ
ijassa-899	424	9	the	the	DET
ijassa-899	424	10	above	above	ADJ
ijassa-899	424	11	methods	method	NOUN
ijassa-899	424	12	can	can	AUX
ijassa-899	424	13	not	not	PART
ijassa-899	424	14	be	be	AUX
ijassa-899	424	15	applied	apply	VERB
ijassa-899	424	16	.	.	PUNCT
ijassa-899	425	1	thus	thus	ADV
ijassa-899	425	2	,	,	PUNCT
ijassa-899	425	3	it	it	PRON
ijassa-899	425	4	is	be	AUX
ijassa-899	425	5	reasonable	reasonable	ADJ
ijassa-899	425	6	to	to	PART
ijassa-899	425	7	develop	develop	VERB
ijassa-899	425	8	algorithms	algorithm	NOUN
ijassa-899	425	9	for	for	ADP
ijassa-899	425	10	detecting	detect	VERB
ijassa-899	425	11	potentially	potentially	ADV
ijassa-899	425	12	incorrect	incorrect	ADJ
ijassa-899	425	13	values	value	NOUN
ijassa-899	425	14	from	from	ADP
ijassa-899	425	15	raw	raw	ADJ
ijassa-899	425	16	data	datum	NOUN
ijassa-899	425	17	.	.	PUNCT
ijassa-899	426	1	for	for	ADP
ijassa-899	426	2	practical	practical	ADJ
ijassa-899	426	3	application	application	NOUN
ijassa-899	426	4	of	of	ADP
ijassa-899	426	5	research	research	NOUN
ijassa-899	426	6	on	on	ADP
ijassa-899	426	7	pvt	pvt	PROPN
ijassa-899	426	8	-	-	PUNCT
ijassa-899	426	9	properties	property	NOUN
ijassa-899	426	10	of	of	ADP
ijassa-899	426	11	fluids	fluid	NOUN
ijassa-899	426	12	,	,	PUNCT
ijassa-899	426	13	a	a	DET
ijassa-899	426	14	database	database	NOUN
ijassa-899	426	15	containing	contain	VERB
ijassa-899	426	16	the	the	DET
ijassa-899	426	17	results	result	NOUN
ijassa-899	426	18	of	of	ADP
ijassa-899	426	19	studies	study	NOUN
ijassa-899	426	20	of	of	ADP
ijassa-899	426	21	more	more	ADJ
ijassa-899	426	22	than	than	ADP
ijassa-899	426	23	3,200	3,200	NUM
ijassa-899	426	24	samples	sample	NOUN
ijassa-899	426	25	of	of	ADP
ijassa-899	426	26	reservoir	reservoir	NOUN
ijassa-899	426	27	fluids	fluid	NOUN
ijassa-899	426	28	was	be	AUX
ijassa-899	426	29	analyzed	analyze	VERB
ijassa-899	426	30	.	.	PUNCT
ijassa-899	427	1	among	among	ADP
ijassa-899	427	2	the	the	DET
ijassa-899	427	3	considered	consider	VERB
ijassa-899	427	4	features	feature	NOUN
ijassa-899	427	5	,	,	PUNCT
ijassa-899	427	6	there	there	PRON
ijassa-899	427	7	are	be	VERB
ijassa-899	427	8	the	the	DET
ijassa-899	427	9	following	follow	VERB
ijassa-899	427	10	values	value	NOUN
ijassa-899	427	11	:	:	PUNCT
ijassa-899	427	12	reservoir	reservoir	NOUN
ijassa-899	427	13	pressure	pressure	NOUN
ijassa-899	427	14	,	,	PUNCT
ijassa-899	427	15	reservoir	reservoir	NOUN
ijassa-899	427	16	temperature	temperature	NOUN
ijassa-899	427	17	,	,	PUNCT
ijassa-899	427	18	surface	surface	NOUN
ijassa-899	427	19	gas	gas	NOUN
ijassa-899	427	20	density	density	NOUN
ijassa-899	427	21	,	,	PUNCT
ijassa-899	427	22	surface	surface	NOUN
ijassa-899	427	23	oil	oil	NOUN
ijassa-899	427	24	density	density	NOUN
ijassa-899	427	25	,	,	PUNCT
ijassa-899	427	26	gas	gas	NOUN
ijassa-899	427	27	content	content	NOUN
ijassa-899	427	28	,	,	PUNCT
ijassa-899	427	29	saturation	saturation	NOUN
ijassa-899	427	30	pressure	pressure	NOUN
ijassa-899	427	31	,	,	PUNCT
ijassa-899	427	32	reservoir	reservoir	NOUN
ijassa-899	427	33	oil	oil	NOUN
ijassa-899	427	34	density	density	NOUN
ijassa-899	427	35	,	,	PUNCT
ijassa-899	427	36	oil	oil	NOUN
ijassa-899	427	37	volume	volume	NOUN
ijassa-899	427	38	coefficient	coefficient	NOUN
ijassa-899	427	39	,	,	PUNCT
ijassa-899	427	40	and	and	CCONJ
ijassa-899	427	41	reservoir	reservoir	NOUN
ijassa-899	427	42	oil	oil	NOUN
ijassa-899	427	43	viscosity	viscosity	NOUN
ijassa-899	427	44	.	.	PUNCT
ijassa-899	428	1	the	the	DET
ijassa-899	428	2	problem	problem	NOUN
ijassa-899	428	3	of	of	ADP
ijassa-899	428	4	predicting	predict	VERB
ijassa-899	428	5	pvt	pvt	PROPN
ijassa-899	428	6	properties	property	NOUN
ijassa-899	428	7	using	use	VERB
ijassa-899	428	8	machine	machine	NOUN
ijassa-899	428	9	learning	learning	NOUN
ijassa-899	428	10	methods	method	NOUN
ijassa-899	428	11	was	be	AUX
ijassa-899	428	12	previously	previously	ADV
ijassa-899	428	13	considered	consider	VERB
ijassa-899	428	14	in	in	ADP
ijassa-899	428	15	a	a	DET
ijassa-899	428	16	very	very	ADV
ijassa-899	428	17	limited	limited	ADJ
ijassa-899	428	18	version	version	NOUN
ijassa-899	428	19	.	.	PUNCT
ijassa-899	429	1	for	for	ADP
ijassa-899	429	2	example	example	NOUN
ijassa-899	429	3	,	,	PUNCT
ijassa-899	429	4	in	in	ADP
ijassa-899	429	5	[	[	X
ijassa-899	429	6	16	16	NUM
ijassa-899	429	7	]	]	PUNCT
ijassa-899	429	8	,	,	PUNCT
ijassa-899	429	9	[	[	X
ijassa-899	429	10	17	17	NUM
ijassa-899	429	11	]	]	PUNCT
ijassa-899	429	12	,	,	PUNCT
ijassa-899	429	13	[	[	X
ijassa-899	429	14	18	18	NUM
ijassa-899	429	15	]	]	PUNCT
ijassa-899	429	16	the	the	DET
ijassa-899	429	17	prediction	prediction	NOUN
ijassa-899	429	18	of	of	ADP
ijassa-899	429	19	saturation	saturation	NOUN
ijassa-899	429	20	pressure	pressure	NOUN
ijassa-899	429	21	through	through	ADP
ijassa-899	429	22	other	other	ADJ
ijassa-899	429	23	properties	property	NOUN
ijassa-899	429	24	using	use	VERB
ijassa-899	429	25	artificial	artificial	ADJ
ijassa-899	429	26	neural	neural	ADJ
ijassa-899	429	27	networks	network	NOUN
ijassa-899	429	28	(	(	PUNCT
ijassa-899	429	29	ann	ann	PROPN
ijassa-899	429	30	)	)	PUNCT
ijassa-899	429	31	is	be	AUX
ijassa-899	429	32	considered	consider	VERB
ijassa-899	429	33	.	.	PUNCT
ijassa-899	430	1	in	in	ADP
ijassa-899	430	2	[	[	X
ijassa-899	430	3	18	18	NUM
ijassa-899	430	4	]	]	PUNCT
ijassa-899	430	5	,	,	PUNCT
ijassa-899	430	6	[	[	X
ijassa-899	430	7	19	19	NUM
ijassa-899	430	8	]	]	ADJ
ijassa-899	430	9	predictions	prediction	NOUN
ijassa-899	430	10	of	of	ADP
ijassa-899	430	11	the	the	DET
ijassa-899	430	12	oil	oil	NOUN
ijassa-899	430	13	volume	volume	NOUN
ijassa-899	430	14	coefficient	coefficient	NOUN
ijassa-899	430	15	are	be	AUX
ijassa-899	430	16	made	make	VERB
ijassa-899	430	17	in	in	ADP
ijassa-899	430	18	the	the	DET
ijassa-899	430	19	same	same	ADJ
ijassa-899	430	20	way	way	NOUN
ijassa-899	430	21	.	.	PUNCT
ijassa-899	431	1	in	in	ADP
ijassa-899	431	2	[	[	X
ijassa-899	431	3	20	20	NUM
ijassa-899	431	4	]	]	PUNCT
ijassa-899	431	5	svm	svm	ADJ
ijassa-899	431	6	regression	regression	NOUN
ijassa-899	431	7	is	be	AUX
ijassa-899	431	8	used	use	VERB
ijassa-899	431	9	to	to	PART
ijassa-899	431	10	predict	predict	VERB
ijassa-899	431	11	the	the	DET
ijassa-899	431	12	features	feature	NOUN
ijassa-899	431	13	mentioned	mention	VERB
ijassa-899	431	14	above	above	ADV
ijassa-899	431	15	.	.	PUNCT
ijassa-899	432	1	this	this	DET
ijassa-899	432	2	paper	paper	NOUN
ijassa-899	432	3	offers	offer	VERB
ijassa-899	432	4	a	a	DET
ijassa-899	432	5	fundamentally	fundamentally	ADV
ijassa-899	432	6	different	different	ADJ
ijassa-899	432	7	approach	approach	NOUN
ijassa-899	432	8	to	to	ADP
ijassa-899	432	9	solving	solve	VERB
ijassa-899	432	10	these	these	DET
ijassa-899	432	11	problems	problem	NOUN
ijassa-899	432	12	.	.	PUNCT
ijassa-899	433	1	it	it	PRON
ijassa-899	433	2	is	be	AUX
ijassa-899	433	3	based	base	VERB
ijassa-899	433	4	on	on	ADP
ijassa-899	433	5	the	the	DET
ijassa-899	433	6	introduction	introduction	NOUN
ijassa-899	433	7	of	of	ADP
ijassa-899	433	8	a	a	DET
ijassa-899	433	9	probabilistic	probabilistic	ADJ
ijassa-899	433	10	model	model	NOUN
ijassa-899	433	11	in	in	ADP
ijassa-899	433	12	the	the	DET
ijassa-899	433	13	space	space	NOUN
ijassa-899	433	14	of	of	ADP
ijassa-899	433	15	reservoir	reservoir	NOUN
ijassa-899	433	16	fluid	fluid	NOUN
ijassa-899	433	17	properties	property	NOUN
ijassa-899	433	18	,	,	PUNCT
ijassa-899	433	19	where	where	SCONJ
ijassa-899	433	20	it	it	PRON
ijassa-899	433	21	is	be	AUX
ijassa-899	433	22	assumed	assume	VERB
ijassa-899	433	23	that	that	SCONJ
ijassa-899	433	24	the	the	DET
ijassa-899	433	25	characteristic	characteristic	ADJ
ijassa-899	433	26	description	description	NOUN
ijassa-899	433	27	of	of	ADP
ijassa-899	433	28	the	the	DET
ijassa-899	433	29	sample	sample	NOUN
ijassa-899	433	30	is	be	AUX
ijassa-899	433	31	obtained	obtain	VERB
ijassa-899	433	32	independently	independently	ADV
ijassa-899	433	33	of	of	ADP
ijassa-899	433	34	all	all	DET
ijassa-899	433	35	other	other	ADJ
ijassa-899	433	36	samples	sample	NOUN
ijassa-899	433	37	from	from	ADP
ijassa-899	433	38	a	a	DET
ijassa-899	433	39	certain	certain	ADJ
ijassa-899	433	40	probability	probability	NOUN
ijassa-899	433	41	distribution	distribution	NOUN
ijassa-899	433	42	.	.	PUNCT
ijassa-899	434	1	6.2	6.2	NUM
ijassa-899	434	2	.	.	X
ijassa-899	434	3	normal	normal	ADJ
ijassa-899	434	4	mixture	mixture	NOUN
ijassa-899	434	5	model	model	NOUN
ijassa-899	434	6	first	first	ADV
ijassa-899	434	7	we	we	PRON
ijassa-899	434	8	use	use	VERB
ijassa-899	434	9	a	a	DET
ijassa-899	434	10	mixture	mixture	NOUN
ijassa-899	434	11	of	of	ADP
ijassa-899	434	12	normal	normal	ADJ
ijassa-899	434	13	distributions	distribution	NOUN
ijassa-899	434	14	of	of	ADP
ijassa-899	434	15	four	four	NUM
ijassa-899	434	16	components	component	NOUN
ijassa-899	434	17	to	to	PART
ijassa-899	434	18	describe	describe	VERB
ijassa-899	434	19	data	datum	NOUN
ijassa-899	434	20	.	.	PUNCT
ijassa-899	435	1	since	since	SCONJ
ijassa-899	435	2	the	the	DET
ijassa-899	435	3	iterative	iterative	NOUN
ijassa-899	435	4	procedure	procedure	NOUN
ijassa-899	435	5	of	of	ADP
ijassa-899	435	6	the	the	DET
ijassa-899	435	7	em	em	PROPN
ijassa-899	435	8	algorithm	algorithm	NOUN
ijassa-899	435	9	converges	converge	VERB
ijassa-899	435	10	to	to	ADP
ijassa-899	435	11	the	the	DET
ijassa-899	435	12	point	point	NOUN
ijassa-899	435	13	of	of	ADP
ijassa-899	435	14	the	the	DET
ijassa-899	435	15	local	local	ADJ
ijassa-899	435	16	maximum	maximum	NOUN
ijassa-899	435	17	of	of	ADP
ijassa-899	435	18	the	the	DET
ijassa-899	435	19	logarithmic	logarithmic	ADJ
ijassa-899	435	20	likelihood	likelihood	NOUN
ijassa-899	435	21	function	function	NOUN
ijassa-899	435	22	,	,	PUNCT
ijassa-899	435	23	the	the	DET
ijassa-899	435	24	method	method	NOUN
ijassa-899	435	25	was	be	AUX
ijassa-899	435	26	run	run	VERB
ijassa-899	435	27	several	several	ADJ
ijassa-899	435	28	times	time	NOUN
ijassa-899	435	29	from	from	ADP
ijassa-899	435	30	random	random	ADJ
ijassa-899	435	31	initial	initial	ADJ
ijassa-899	435	32	values	value	NOUN
ijassa-899	435	33	of	of	ADP
ijassa-899	435	34	parameters	parameter	NOUN
ijassa-899	435	35	.	.	PUNCT
ijassa-899	436	1	the	the	DET
ijassa-899	436	2	final	final	ADJ
ijassa-899	436	3	score	score	NOUN
ijassa-899	436	4	is	be	AUX
ijassa-899	436	5	obtained	obtain	VERB
ijassa-899	436	6	in	in	ADP
ijassa-899	436	7	the	the	DET
ijassa-899	436	8	iteration	iteration	NOUN
ijassa-899	436	9	with	with	ADP
ijassa-899	436	10	the	the	DET
ijassa-899	436	11	highest	high	ADJ
ijassa-899	436	12	value	value	NOUN
ijassa-899	436	13	of	of	ADP
ijassa-899	436	14	the	the	DET
ijassa-899	436	15	variational	variational	ADJ
ijassa-899	436	16	lower	low	ADJ
ijassa-899	436	17	score	score	NOUN
ijassa-899	436	18	.	.	PUNCT
ijassa-899	437	1	the	the	DET
ijassa-899	437	2	result	result	NOUN
ijassa-899	437	3	of	of	ADP
ijassa-899	437	4	parameter	parameter	NOUN
ijassa-899	437	5	estimation	estimation	NOUN
ijassa-899	437	6	is	be	AUX
ijassa-899	437	7	shown	show	VERB
ijassa-899	437	8	on	on	ADP
ijassa-899	437	9	figure	figure	NOUN
ijassa-899	437	10	6.1	6.1	NUM
ijassa-899	437	11	.	.	PUNCT
ijassa-899	438	1	the	the	DET
ijassa-899	438	2	diagonal	diagonal	ADJ
ijassa-899	438	3	shows	show	VERB
ijassa-899	438	4	the	the	DET
ijassa-899	438	5	densities	density	NOUN
ijassa-899	438	6	of	of	ADP
ijassa-899	438	7	features	feature	NOUN
ijassa-899	438	8	for	for	ADP
ijassa-899	438	9	the	the	DET
ijassa-899	438	10	estimated	estimate	VERB
ijassa-899	438	11	mixture	mixture	NOUN
ijassa-899	438	12	.	.	PUNCT
ijassa-899	439	1	each	each	DET
ijassa-899	439	2	non	non	ADJ
ijassa-899	439	3	-	-	ADJ
ijassa-899	439	4	diagonal	diagonal	ADJ
ijassa-899	439	5	cell	cell	NOUN
ijassa-899	439	6	on	on	ADP
ijassa-899	439	7	figure	figure	NOUN
ijassa-899	439	8	corresponds	correspond	NOUN
ijassa-899	439	9	to	to	ADP
ijassa-899	439	10	the	the	DET
ijassa-899	439	11	projection	projection	NOUN
ijassa-899	439	12	of	of	ADP
ijassa-899	439	13	the	the	DET
ijassa-899	439	14	feature	feature	NOUN
ijassa-899	439	15	space	space	NOUN
ijassa-899	439	16	on	on	ADP
ijassa-899	439	17	all	all	DET
ijassa-899	439	18	possible	possible	ADJ
ijassa-899	439	19	coordinate	coordinate	NOUN
ijassa-899	439	20	planes	plane	NOUN
ijassa-899	439	21	.	.	PUNCT
ijassa-899	440	1	lines	line	NOUN
ijassa-899	440	2	of	of	ADP
ijassa-899	440	3	the	the	DET
ijassa-899	440	4	density	density	NOUN
ijassa-899	440	5	level	level	NOUN
ijassa-899	440	6	of	of	ADP
ijassa-899	440	7	the	the	DET
ijassa-899	440	8	resulting	result	VERB
ijassa-899	440	9	mixture	mixture	NOUN
ijassa-899	440	10	of	of	ADP
ijassa-899	440	11	distributions	distribution	NOUN
ijassa-899	440	12	are	be	AUX
ijassa-899	440	13	drawn	draw	VERB
ijassa-899	440	14	above	above	ADP
ijassa-899	440	15	the	the	DET
ijassa-899	440	16	diagonal	diagonal	NOUN
ijassa-899	440	17	.	.	PUNCT
ijassa-899	441	1	below	below	ADP
ijassa-899	441	2	the	the	DET
ijassa-899	441	3	diagonal	diagonal	ADJ
ijassa-899	441	4	,	,	PUNCT
ijassa-899	441	5	ellipses	ellipsis	NOUN
ijassa-899	441	6	of	of	ADP
ijassa-899	441	7	different	different	ADJ
ijassa-899	441	8	colors	color	NOUN
ijassa-899	441	9	indicate	indicate	VERB
ijassa-899	441	10	the	the	DET
ijassa-899	441	11	relative	relative	ADJ
ijassa-899	441	12	location	location	NOUN
ijassa-899	441	13	of	of	ADP
ijassa-899	441	14	clusters	cluster	NOUN
ijassa-899	441	15	.	.	PUNCT
ijassa-899	442	1	notice	notice	VERB
ijassa-899	442	2	that	that	SCONJ
ijassa-899	442	3	a	a	DET
ijassa-899	442	4	gray	gray	ADJ
ijassa-899	442	5	semi	semi	ADJ
ijassa-899	442	6	-	-	ADJ
ijassa-899	442	7	transparent	transparent	ADJ
ijassa-899	442	8	cluster	cluster	NOUN
ijassa-899	442	9	covers	cover	VERB
ijassa-899	442	10	other	other	ADJ
ijassa-899	442	11	two	two	NUM
ijassa-899	442	12	clusters	cluster	NOUN
ijassa-899	442	13	,	,	PUNCT
ijassa-899	442	14	due	due	ADP
ijassa-899	442	15	to	to	ADP
ijassa-899	442	16	the	the	DET
ijassa-899	442	17	presence	presence	NOUN
ijassa-899	442	18	of	of	ADP
ijassa-899	442	19	noise	noise	NOUN
ijassa-899	442	20	objects	object	NOUN
ijassa-899	442	21	in	in	ADP
ijassa-899	442	22	the	the	DET
ijassa-899	442	23	data	datum	NOUN
ijassa-899	442	24	.	.	PUNCT
ijassa-899	443	1	the	the	DET
ijassa-899	443	2	normal	normal	ADJ
ijassa-899	443	3	distribution	distribution	NOUN
ijassa-899	443	4	has	have	VERB
ijassa-899	443	5	light	light	ADJ
ijassa-899	443	6	tails	tail	NOUN
ijassa-899	443	7	,	,	PUNCT
ijassa-899	443	8	so	so	SCONJ
ijassa-899	443	9	the	the	DET
ijassa-899	443	10	estimations	estimation	NOUN
ijassa-899	443	11	of	of	ADP
ijassa-899	443	12	its	its	PRON
ijassa-899	443	13	parameters	parameter	NOUN
ijassa-899	443	14	are	be	AUX
ijassa-899	443	15	not	not	PART
ijassa-899	443	16	stable	stable	ADJ
ijassa-899	443	17	to	to	ADP
ijassa-899	443	18	the	the	DET
ijassa-899	443	19	presence	presence	NOUN
ijassa-899	443	20	of	of	ADP
ijassa-899	443	21	noise	noise	NOUN
ijassa-899	443	22	objects	object	NOUN
ijassa-899	443	23	in	in	ADP
ijassa-899	443	24	the	the	DET
ijassa-899	443	25	data	datum	NOUN
ijassa-899	443	26	.	.	PUNCT
ijassa-899	444	1	when	when	SCONJ
ijassa-899	444	2	constructing	construct	VERB
ijassa-899	444	3	a	a	DET
ijassa-899	444	4	model	model	NOUN
ijassa-899	444	5	of	of	ADP
ijassa-899	444	6	a	a	DET
ijassa-899	444	7	mixture	mixture	NOUN
ijassa-899	444	8	of	of	ADP
ijassa-899	444	9	normal	normal	ADJ
ijassa-899	444	10	distributions	distribution	NOUN
ijassa-899	444	11	on	on	ADP
ijassa-899	444	12	pvt	pvt	PROPN
ijassa-899	444	13	data	data	PROPN
ijassa-899	444	14	,	,	PUNCT
ijassa-899	444	15	the	the	DET
ijassa-899	444	16	em	em	PROPN
ijassa-899	444	17	algorithm	algorithm	NOUN
ijassa-899	444	18	tries	try	VERB
ijassa-899	444	19	to	to	PART
ijassa-899	444	20	describe	describe	VERB
ijassa-899	444	21	the	the	DET
ijassa-899	444	22	main	main	ADJ
ijassa-899	444	23	part	part	NOUN
ijassa-899	444	24	of	of	ADP
ijassa-899	444	25	the	the	DET
ijassa-899	444	26	data	datum	NOUN
ijassa-899	444	27	using	use	VERB
ijassa-899	444	28	three	three	NUM
ijassa-899	444	29	clusters	cluster	NOUN
ijassa-899	444	30	,	,	PUNCT
ijassa-899	444	31	and	and	CCONJ
ijassa-899	444	32	the	the	DET
ijassa-899	444	33	other	other	ADJ
ijassa-899	444	34	less	less	ADV
ijassa-899	444	35	typical	typical	ADJ
ijassa-899	444	36	objects	object	NOUN
ijassa-899	444	37	using	use	VERB
ijassa-899	444	38	the	the	DET
ijassa-899	444	39	fourth	fourth	ADJ
ijassa-899	444	40	one	one	NUM
ijassa-899	444	41	.	.	PUNCT
ijassa-899	445	1	copyright	copyright	NOUN
ijassa-899	445	2	©	©	PROPN
ijassa-899	445	3	2020	2020	NUM
ijassa-899	445	4	assa	assa	NOUN
ijassa-899	445	5	.	.	PUNCT
ijassa-899	446	1	adv	adv	PROPN
ijassa-899	446	2	syst	syst	PROPN
ijassa-899	446	3	sci	sci	PROPN
ijassa-899	446	4	appl	appl	PROPN
ijassa-899	446	5	(	(	PUNCT
ijassa-899	446	6	2020	2020	NUM
ijassa-899	446	7	)	)	PUNCT
ijassa-899	446	8	112	112	NUM
ijassa-899	446	9	n.a	n.a	PROPN
ijassa-899	446	10	.	.	PROPN
ijassa-899	446	11	volkov	volkov	PROPN
ijassa-899	446	12	,	,	PUNCT
ijassa-899	446	13	e.yu	e.yu	PROPN
ijassa-899	446	14	.	.	PROPN
ijassa-899	446	15	dakhova	dakhova	PROPN
ijassa-899	446	16	,	,	PUNCT
ijassa-899	446	17	s.a	s.a	PROPN
ijassa-899	446	18	.	.	PROPN
ijassa-899	446	19	budennyy	budennyy	PROPN
ijassa-899	446	20	,	,	PUNCT
ijassa-899	446	21	a.m.	a.m.	PROPN
ijassa-899	446	22	andrianova	andrianova	PROPN
ijassa-899	446	23	fig	fig	PROPN
ijassa-899	446	24	.	.	PUNCT
ijassa-899	447	1	6.1	6.1	NUM
ijassa-899	447	2	.	.	PUNCT
ijassa-899	447	3	data	datum	NOUN
ijassa-899	447	4	visualization	visualization	NOUN
ijassa-899	447	5	and	and	CCONJ
ijassa-899	447	6	results	result	NOUN
ijassa-899	447	7	of	of	ADP
ijassa-899	447	8	application	application	NOUN
ijassa-899	447	9	of	of	ADP
ijassa-899	447	10	the	the	DET
ijassa-899	447	11	normal	normal	ADJ
ijassa-899	447	12	mixture	mixture	NOUN
ijassa-899	447	13	model	model	NOUN
ijassa-899	447	14	.	.	PUNCT
ijassa-899	448	1	the	the	DET
ijassa-899	448	2	plots	plot	NOUN
ijassa-899	448	3	above	above	ADP
ijassa-899	448	4	the	the	DET
ijassa-899	448	5	diagonal	diagonal	ADJ
ijassa-899	448	6	show	show	NOUN
ijassa-899	448	7	the	the	DET
ijassa-899	448	8	density	density	NOUN
ijassa-899	448	9	levels	level	NOUN
ijassa-899	448	10	of	of	ADP
ijassa-899	448	11	joint	joint	ADJ
ijassa-899	448	12	distribution	distribution	NOUN
ijassa-899	448	13	of	of	ADP
ijassa-899	448	14	features	feature	NOUN
ijassa-899	448	15	projected	project	VERB
ijassa-899	448	16	on	on	ADP
ijassa-899	448	17	subspace	subspace	NOUN
ijassa-899	448	18	of	of	ADP
ijassa-899	448	19	every	every	DET
ijassa-899	448	20	two	two	NUM
ijassa-899	448	21	features	feature	NOUN
ijassa-899	448	22	.	.	PUNCT
ijassa-899	449	1	the	the	DET
ijassa-899	449	2	diagonal	diagonal	ADJ
ijassa-899	449	3	plots	plot	NOUN
ijassa-899	449	4	show	show	VERB
ijassa-899	449	5	the	the	DET
ijassa-899	449	6	density	density	NOUN
ijassa-899	449	7	of	of	ADP
ijassa-899	449	8	every	every	DET
ijassa-899	449	9	feature	feature	NOUN
ijassa-899	449	10	.	.	PUNCT
ijassa-899	450	1	the	the	DET
ijassa-899	450	2	plots	plot	NOUN
ijassa-899	450	3	under	under	ADP
ijassa-899	450	4	the	the	DET
ijassa-899	450	5	diagonal	diagonal	ADJ
ijassa-899	450	6	show	show	NOUN
ijassa-899	450	7	the	the	DET
ijassa-899	450	8	clusterization	clusterization	NOUN
ijassa-899	450	9	based	base	VERB
ijassa-899	450	10	on	on	ADP
ijassa-899	450	11	the	the	DET
ijassa-899	450	12	application	application	NOUN
ijassa-899	450	13	of	of	ADP
ijassa-899	450	14	the	the	DET
ijassa-899	450	15	normal	normal	ADJ
ijassa-899	450	16	mixture	mixture	NOUN
ijassa-899	450	17	model	model	NOUN
ijassa-899	450	18	.	.	PUNCT
ijassa-899	451	1	every	every	DET
ijassa-899	451	2	ellipse	ellipse	NOUN
ijassa-899	451	3	corresponds	correspond	VERB
ijassa-899	451	4	to	to	ADP
ijassa-899	451	5	one	one	NUM
ijassa-899	451	6	of	of	ADP
ijassa-899	451	7	four	four	NUM
ijassa-899	451	8	clusters	cluster	NOUN
ijassa-899	451	9	/	/	SYM
ijassa-899	451	10	components	component	NOUN
ijassa-899	451	11	of	of	ADP
ijassa-899	451	12	the	the	DET
ijassa-899	451	13	mixture	mixture	NOUN
ijassa-899	451	14	6.3	6.3	NUM
ijassa-899	451	15	.	.	PUNCT
ijassa-899	452	1	student	student	NOUN
ijassa-899	452	2	mixture	mixture	NOUN
ijassa-899	452	3	model	model	NOUN
ijassa-899	452	4	to	to	PART
ijassa-899	452	5	eliminate	eliminate	VERB
ijassa-899	452	6	the	the	DET
ijassa-899	452	7	above	above	ADJ
ijassa-899	452	8	disadvantages	disadvantage	NOUN
ijassa-899	452	9	,	,	PUNCT
ijassa-899	452	10	the	the	DET
ijassa-899	452	11	student	student	NOUN
ijassa-899	452	12	distribution	distribution	NOUN
ijassa-899	452	13	mixture	mixture	NOUN
ijassa-899	452	14	model	model	NOUN
ijassa-899	452	15	is	be	AUX
ijassa-899	452	16	applied	apply	VERB
ijassa-899	452	17	.	.	PUNCT
ijassa-899	453	1	the	the	DET
ijassa-899	453	2	number	number	NOUN
ijassa-899	453	3	of	of	ADP
ijassa-899	453	4	degrees	degree	NOUN
ijassa-899	453	5	of	of	ADP
ijassa-899	453	6	freedom	freedom	NOUN
ijassa-899	453	7	nu	nu	PROPN
ijassa-899	453	8	needs	need	VERB
ijassa-899	453	9	some	some	DET
ijassa-899	453	10	expertise	expertise	NOUN
ijassa-899	453	11	.	.	PUNCT
ijassa-899	454	1	on	on	ADP
ijassa-899	454	2	figure	figure	NOUN
ijassa-899	454	3	6.2	6.2	NUM
ijassa-899	454	4	the	the	DET
ijassa-899	454	5	dependence	dependence	NOUN
ijassa-899	454	6	of	of	ADP
ijassa-899	454	7	the	the	DET
ijassa-899	454	8	variational	variational	ADV
ijassa-899	454	9	lower	lower	ADV
ijassa-899	454	10	bound	bind	VERB
ijassa-899	454	11	on	on	ADP
ijassa-899	454	12	the	the	DET
ijassa-899	454	13	iteration	iteration	NOUN
ijassa-899	454	14	for	for	ADP
ijassa-899	454	15	the	the	DET
ijassa-899	454	16	best	good	ADJ
ijassa-899	454	17	result	result	NOUN
ijassa-899	454	18	among	among	ADP
ijassa-899	454	19	several	several	ADJ
ijassa-899	454	20	runs	run	NOUN
ijassa-899	454	21	from	from	ADP
ijassa-899	454	22	different	different	ADJ
ijassa-899	454	23	initial	initial	ADJ
ijassa-899	454	24	approximations	approximation	NOUN
ijassa-899	454	25	is	be	AUX
ijassa-899	454	26	shown	show	VERB
ijassa-899	454	27	.	.	PUNCT
ijassa-899	455	1	the	the	DET
ijassa-899	455	2	result	result	NOUN
ijassa-899	455	3	of	of	ADP
ijassa-899	455	4	evaluating	evaluate	VERB
ijassa-899	455	5	parameters	parameter	NOUN
ijassa-899	455	6	for	for	ADP
ijassa-899	455	7	four	four	NUM
ijassa-899	455	8	clusters	cluster	NOUN
ijassa-899	455	9	is	be	AUX
ijassa-899	455	10	shown	show	VERB
ijassa-899	455	11	on	on	ADP
ijassa-899	455	12	figure	figure	NOUN
ijassa-899	455	13	6.3	6.3	NUM
ijassa-899	455	14	.	.	PUNCT
ijassa-899	456	1	the	the	DET
ijassa-899	456	2	blue	blue	ADJ
ijassa-899	456	3	and	and	CCONJ
ijassa-899	456	4	red	red	ADJ
ijassa-899	456	5	clusters	cluster	NOUN
ijassa-899	456	6	obtained	obtain	VERB
ijassa-899	456	7	using	use	VERB
ijassa-899	456	8	the	the	DET
ijassa-899	456	9	student	student	NOUN
ijassa-899	456	10	multidimensional	multidimensional	ADJ
ijassa-899	456	11	distribution	distribution	NOUN
ijassa-899	456	12	mixture	mixture	NOUN
ijassa-899	456	13	model	model	NOUN
ijassa-899	456	14	correspond	correspond	VERB
ijassa-899	456	15	to	to	ADP
ijassa-899	456	16	the	the	DET
ijassa-899	456	17	same	same	ADJ
ijassa-899	456	18	clusters	cluster	NOUN
ijassa-899	456	19	in	in	ADP
ijassa-899	456	20	the	the	DET
ijassa-899	456	21	multi	multi	ADJ
ijassa-899	456	22	-	-	ADJ
ijassa-899	456	23	dimensional	dimensional	ADJ
ijassa-899	456	24	normal	normal	ADJ
ijassa-899	456	25	distribution	distribution	NOUN
ijassa-899	456	26	mixture	mixture	NOUN
ijassa-899	456	27	model	model	NOUN
ijassa-899	456	28	.	.	PUNCT
ijassa-899	457	1	the	the	DET
ijassa-899	457	2	green	green	ADJ
ijassa-899	457	3	cluster	cluster	NOUN
ijassa-899	457	4	is	be	AUX
ijassa-899	457	5	split	split	VERB
ijassa-899	457	6	into	into	ADP
ijassa-899	457	7	two	two	NUM
ijassa-899	457	8	.	.	PUNCT
ijassa-899	458	1	note	note	VERB
ijassa-899	458	2	the	the	DET
ijassa-899	458	3	clusters	cluster	NOUN
ijassa-899	458	4	have	have	VERB
ijassa-899	458	5	no	no	DET
ijassa-899	458	6	noise	noise	NOUN
ijassa-899	458	7	.	.	PUNCT
ijassa-899	459	1	this	this	DET
ijassa-899	459	2	result	result	NOUN
ijassa-899	459	3	is	be	AUX
ijassa-899	459	4	a	a	DET
ijassa-899	459	5	consequence	consequence	NOUN
ijassa-899	459	6	of	of	ADP
ijassa-899	459	7	the	the	DET
ijassa-899	459	8	stability	stability	NOUN
ijassa-899	459	9	of	of	ADP
ijassa-899	459	10	the	the	DET
ijassa-899	459	11	student	student	NOUN
ijassa-899	459	12	distribution	distribution	NOUN
ijassa-899	459	13	to	to	ADP
ijassa-899	459	14	emissions	emission	NOUN
ijassa-899	459	15	.	.	PUNCT
ijassa-899	460	1	copyright	copyright	NOUN
ijassa-899	460	2	©	©	PROPN
ijassa-899	460	3	2020	2020	NUM
ijassa-899	460	4	assa	assa	NOUN
ijassa-899	460	5	.	.	PUNCT
ijassa-899	461	1	adv	adv	PROPN
ijassa-899	461	2	syst	syst	PROPN
ijassa-899	461	3	sci	sci	PROPN
ijassa-899	461	4	appl	appl	PROPN
ijassa-899	461	5	(	(	PUNCT
ijassa-899	461	6	2020	2020	NUM
ijassa-899	461	7	)	)	PUNCT
ijassa-899	461	8	student	student	NOUN
ijassa-899	461	9	mixture	mixture	NOUN
ijassa-899	461	10	and	and	CCONJ
ijassa-899	461	11	its	its	PRON
ijassa-899	461	12	machine	machine	NOUN
ijassa-899	461	13	learning	learn	VERB
ijassa-899	461	14	applications	application	NOUN
ijassa-899	461	15	to	to	ADP
ijassa-899	461	16	pvt	pvt	PROPN
ijassa-899	461	17	properties	property	NOUN
ijassa-899	461	18	113	113	NUM
ijassa-899	461	19	fig	fig	NOUN
ijassa-899	461	20	.	.	PUNCT
ijassa-899	462	1	6.2	6.2	NUM
ijassa-899	462	2	.	.	PUNCT
ijassa-899	463	1	the	the	DET
ijassa-899	463	2	dependence	dependence	NOUN
ijassa-899	463	3	of	of	ADP
ijassa-899	463	4	the	the	DET
ijassa-899	463	5	variational	variational	ADV
ijassa-899	463	6	lower	lower	ADV
ijassa-899	463	7	bound	bind	VERB
ijassa-899	463	8	on	on	ADP
ijassa-899	463	9	iteration	iteration	NOUN
ijassa-899	463	10	number	number	NOUN
ijassa-899	463	11	table	table	NOUN
ijassa-899	463	12	6.1	6.1	NUM
ijassa-899	463	13	.	.	PUNCT
ijassa-899	464	1	centers	center	NOUN
ijassa-899	464	2	of	of	ADP
ijassa-899	464	3	clusters	cluster	NOUN
ijassa-899	464	4	obtained	obtain	VERB
ijassa-899	464	5	using	use	VERB
ijassa-899	464	6	the	the	DET
ijassa-899	464	7	student	student	NOUN
ijassa-899	464	8	mixture	mixture	NOUN
ijassa-899	464	9	model	model	NOUN
ijassa-899	464	10	cluster	cluster	NOUN
ijassa-899	464	11	0	0	NUM
ijassa-899	464	12	cluster	cluster	NOUN
ijassa-899	464	13	1	1	NUM
ijassa-899	464	14	cluster	cluster	NOUN
ijassa-899	464	15	2	2	NUM
ijassa-899	464	16	cluster	cluster	NOUN
ijassa-899	464	17	3	3	NUM
ijassa-899	464	18	sample	sample	NOUN
ijassa-899	464	19	rate	rate	NOUN
ijassa-899	464	20	28.3	28.3	NUM
ijassa-899	464	21	%	%	NOUN
ijassa-899	464	22	27.7	27.7	NUM
ijassa-899	464	23	%	%	NOUN
ijassa-899	464	24	29.0	29.0	NUM
ijassa-899	464	25	%	%	NOUN
ijassa-899	464	26	15.0	15.0	NUM
ijassa-899	464	27	%	%	NOUN
ijassa-899	464	28	reservoir	reservoir	NOUN
ijassa-899	464	29	pressure	pressure	NOUN
ijassa-899	464	30	,	,	PUNCT
ijassa-899	464	31	mpa	mpa	PROPN
ijassa-899	464	32	23.86	23.86	NUM
ijassa-899	464	33	25.36	25.36	NUM
ijassa-899	464	34	25.63	25.63	NUM
ijassa-899	464	35	36.04	36.04	NUM
ijassa-899	464	36	reservoir	reservoir	NOUN
ijassa-899	464	37	temperature	temperature	NOUN
ijassa-899	464	38	,	,	PUNCT
ijassa-899	464	39	oc	oc	ADP
ijassa-899	464	40	75.33	75.33	NUM
ijassa-899	464	41	80.47	80.47	NUM
ijassa-899	464	42	83.91	83.91	NUM
ijassa-899	464	43	96.12	96.12	NUM
ijassa-899	464	44	surface	surface	NOUN
ijassa-899	464	45	gas	gas	NOUN
ijassa-899	464	46	density	density	NOUN
ijassa-899	464	47	,	,	PUNCT
ijassa-899	464	48	kg	kg	PROPN
ijassa-899	464	49	/	/	SYM
ijassa-899	464	50	m3	m3	PROPN
ijassa-899	464	51	1.01	1.01	NUM
ijassa-899	464	52	1.07	1.07	NUM
ijassa-899	464	53	1.17	1.17	NUM
ijassa-899	464	54	1.11	1.11	NUM
ijassa-899	464	55	surface	surface	NOUN
ijassa-899	464	56	oil	oil	NOUN
ijassa-899	464	57	density	density	NOUN
ijassa-899	464	58	,	,	PUNCT
ijassa-899	464	59	kg	kg	PROPN
ijassa-899	464	60	/	/	SYM
ijassa-899	464	61	m3	m3	PROPN
ijassa-899	464	62	859.74	859.74	NUM
ijassa-899	464	63	855.61	855.61	NUM
ijassa-899	464	64	836.75	836.75	NUM
ijassa-899	464	65	823.55	823.55	NUM
ijassa-899	464	66	gas	gas	NOUN
ijassa-899	464	67	content	content	NOUN
ijassa-899	464	68	,	,	PUNCT
ijassa-899	464	69	m3	m3	PROPN
ijassa-899	464	70	/	/	SYM
ijassa-899	464	71	t	t	PROPN
ijassa-899	464	72	49.20	49.20	NUM
ijassa-899	464	73	69.58	69.58	NUM
ijassa-899	464	74	138.74	138.74	NUM
ijassa-899	464	75	425.62	425.62	NUM
ijassa-899	464	76	saturation	saturation	NOUN
ijassa-899	464	77	pressure	pressure	NOUN
ijassa-899	464	78	,	,	PUNCT
ijassa-899	464	79	mpa	mpa	PROPN
ijassa-899	464	80	8.46	8.46	NUM
ijassa-899	464	81	11.25	11.25	NUM
ijassa-899	464	82	13.61	13.61	NUM
ijassa-899	464	83	24.49	24.49	NUM
ijassa-899	464	84	oil	oil	NOUN
ijassa-899	464	85	reservoir	reservoir	NOUN
ijassa-899	464	86	density	density	NOUN
ijassa-899	464	87	,	,	PUNCT
ijassa-899	464	88	kg	kg	PROPN
ijassa-899	464	89	/	/	SYM
ijassa-899	464	90	m3	m3	PROPN
ijassa-899	464	91	808.33	808.33	NUM
ijassa-899	464	92	772.59	772.59	NUM
ijassa-899	464	93	716.74	716.74	NUM
ijassa-899	464	94	598.29	598.29	NUM
ijassa-899	464	95	volume	volume	NOUN
ijassa-899	464	96	coefficient	coefficient	NOUN
ijassa-899	464	97	.	.	PUNCT
ijassa-899	465	1	oil	oil	NOUN
ijassa-899	465	2	,	,	PUNCT
ijassa-899	465	3	m3	m3	PROPN
ijassa-899	465	4	/	/	SYM
ijassa-899	465	5	m3	m3	PROPN
ijassa-899	465	6	1.12	1.12	NUM
ijassa-899	465	7	1.20	1.20	NUM
ijassa-899	465	8	1.36	1.36	NUM
ijassa-899	465	9	2.04	2.04	NUM
ijassa-899	465	10	reservoir	reservoir	NOUN
ijassa-899	465	11	oil	oil	NOUN
ijassa-899	465	12	viscosity	viscosity	NOUN
ijassa-899	465	13	,	,	PUNCT
ijassa-899	465	14	mpa	mpa	PROPN
ijassa-899	465	15	*	*	PUNCT
ijassa-899	465	16	s	s	PROPN
ijassa-899	465	17	2.29	2.29	NUM
ijassa-899	465	18	1.24	1.24	NUM
ijassa-899	465	19	0.67	0.67	NUM
ijassa-899	465	20	0.27	0.27	NUM
ijassa-899	465	21	oil	oil	NOUN
ijassa-899	465	22	type	type	NOUN
ijassa-899	465	23	heavy	heavy	ADJ
ijassa-899	465	24	&	&	CCONJ
ijassa-899	465	25	medium	medium	ADJ
ijassa-899	465	26	medium	medium	ADJ
ijassa-899	465	27	light	light	ADJ
ijassa-899	465	28	extra	extra	ADJ
ijassa-899	465	29	light	light	ADJ
ijassa-899	465	30	cluster	cluster	NOUN
ijassa-899	465	31	centers	center	NOUN
ijassa-899	465	32	are	be	AUX
ijassa-899	465	33	given	give	VERB
ijassa-899	465	34	in	in	ADP
ijassa-899	465	35	table	table	NOUN
ijassa-899	465	36	6.1	6.1	NUM
ijassa-899	465	37	.	.	PUNCT
ijassa-899	466	1	during	during	ADP
ijassa-899	466	2	training	training	NOUN
ijassa-899	466	3	,	,	PUNCT
ijassa-899	466	4	clusters	cluster	NOUN
ijassa-899	466	5	are	be	AUX
ijassa-899	466	6	defined	define	VERB
ijassa-899	466	7	by	by	ADP
ijassa-899	466	8	the	the	DET
ijassa-899	466	9	model	model	NOUN
ijassa-899	466	10	up	up	ADP
ijassa-899	466	11	to	to	ADP
ijassa-899	466	12	permutation	permutation	NOUN
ijassa-899	466	13	so	so	SCONJ
ijassa-899	466	14	the	the	DET
ijassa-899	466	15	order	order	NOUN
ijassa-899	466	16	of	of	ADP
ijassa-899	466	17	clusters	cluster	NOUN
ijassa-899	466	18	is	be	AUX
ijassa-899	466	19	determined	determine	VERB
ijassa-899	466	20	by	by	ADP
ijassa-899	466	21	experts	expert	NOUN
ijassa-899	466	22	.	.	PUNCT
ijassa-899	467	1	one	one	PRON
ijassa-899	467	2	can	can	AUX
ijassa-899	467	3	notice	notice	VERB
ijassa-899	467	4	that	that	SCONJ
ijassa-899	467	5	for	for	ADP
ijassa-899	467	6	most	most	ADJ
ijassa-899	467	7	clusters	cluster	NOUN
ijassa-899	467	8	their	their	PRON
ijassa-899	467	9	centers	center	NOUN
ijassa-899	467	10	are	be	AUX
ijassa-899	467	11	strictly	strictly	ADV
ijassa-899	467	12	ordered	order	VERB
ijassa-899	467	13	by	by	ADP
ijassa-899	467	14	most	most	ADJ
ijassa-899	467	15	attributes	attribute	NOUN
ijassa-899	467	16	.	.	PUNCT
ijassa-899	468	1	the	the	DET
ijassa-899	468	2	type	type	NOUN
ijassa-899	468	3	of	of	ADP
ijassa-899	468	4	oil	oil	NOUN
ijassa-899	468	5	corresponding	correspond	VERB
ijassa-899	468	6	to	to	ADP
ijassa-899	468	7	each	each	DET
ijassa-899	468	8	cluster	cluster	NOUN
ijassa-899	468	9	is	be	AUX
ijassa-899	468	10	also	also	ADV
ijassa-899	468	11	determined	determine	VERB
ijassa-899	468	12	by	by	ADP
ijassa-899	468	13	experts	expert	NOUN
ijassa-899	468	14	.	.	PUNCT
ijassa-899	469	1	7	7	X
ijassa-899	469	2	.	.	X
ijassa-899	469	3	model	model	PROPN
ijassa-899	469	4	research	research	NOUN
ijassa-899	469	5	the	the	DET
ijassa-899	469	6	behavior	behavior	NOUN
ijassa-899	469	7	of	of	ADP
ijassa-899	469	8	the	the	DET
ijassa-899	469	9	model	model	NOUN
ijassa-899	469	10	based	base	VERB
ijassa-899	469	11	on	on	ADP
ijassa-899	469	12	a	a	DET
ijassa-899	469	13	mixture	mixture	NOUN
ijassa-899	469	14	of	of	ADP
ijassa-899	469	15	four	four	NUM
ijassa-899	469	16	components	component	NOUN
ijassa-899	469	17	of	of	ADP
ijassa-899	469	18	student	student	NOUN
ijassa-899	469	19	distributions	distribution	NOUN
ijassa-899	469	20	is	be	AUX
ijassa-899	469	21	tested	test	VERB
ijassa-899	469	22	on	on	ADP
ijassa-899	469	23	artificial	artificial	ADJ
ijassa-899	469	24	data	datum	NOUN
ijassa-899	469	25	,	,	PUNCT
ijassa-899	469	26	the	the	DET
ijassa-899	469	27	results	result	NOUN
ijassa-899	469	28	of	of	ADP
ijassa-899	469	29	experiments	experiment	NOUN
ijassa-899	469	30	are	be	AUX
ijassa-899	469	31	given	give	VERB
ijassa-899	469	32	below	below	ADV
ijassa-899	469	33	.	.	PUNCT
ijassa-899	470	1	in	in	ADP
ijassa-899	470	2	addition	addition	NOUN
ijassa-899	470	3	,	,	PUNCT
ijassa-899	470	4	the	the	DET
ijassa-899	470	5	quality	quality	NOUN
ijassa-899	470	6	of	of	ADP
ijassa-899	470	7	model	model	NOUN
ijassa-899	470	8	predictions	prediction	NOUN
ijassa-899	470	9	is	be	AUX
ijassa-899	470	10	tested	test	VERB
ijassa-899	470	11	by	by	ADP
ijassa-899	470	12	test	test	NOUN
ijassa-899	470	13	data	datum	NOUN
ijassa-899	470	14	that	that	PRON
ijassa-899	470	15	is	be	AUX
ijassa-899	470	16	not	not	PART
ijassa-899	470	17	involved	involve	VERB
ijassa-899	470	18	in	in	ADP
ijassa-899	470	19	the	the	DET
ijassa-899	470	20	training	training	NOUN
ijassa-899	470	21	set	set	NOUN
ijassa-899	470	22	.	.	PUNCT
ijassa-899	471	1	7.1	7.1	NUM
ijassa-899	471	2	.	.	PUNCT
ijassa-899	472	1	artificial	artificial	ADJ
ijassa-899	472	2	experiments	experiment	NOUN
ijassa-899	472	3	in	in	ADP
ijassa-899	472	4	our	our	PRON
ijassa-899	472	5	research	research	NOUN
ijassa-899	472	6	,	,	PUNCT
ijassa-899	472	7	the	the	DET
ijassa-899	472	8	results	result	NOUN
ijassa-899	472	9	of	of	ADP
ijassa-899	472	10	the	the	DET
ijassa-899	472	11	model	model	NOUN
ijassa-899	472	12	application	application	NOUN
ijassa-899	472	13	to	to	ADP
ijassa-899	472	14	artificial	artificial	ADJ
ijassa-899	472	15	samples	sample	NOUN
ijassa-899	472	16	are	be	AUX
ijassa-899	472	17	analyzed	analyze	VERB
ijassa-899	472	18	.	.	PUNCT
ijassa-899	473	1	some	some	DET
ijassa-899	473	2	sample	sample	NOUN
ijassa-899	473	3	features	feature	NOUN
ijassa-899	473	4	are	be	AUX
ijassa-899	473	5	fixed	fix	VERB
ijassa-899	473	6	in	in	ADP
ijassa-899	473	7	three	three	NUM
ijassa-899	473	8	different	different	ADJ
ijassa-899	473	9	versions	version	NOUN
ijassa-899	473	10	of	of	ADP
ijassa-899	473	11	the	the	DET
ijassa-899	473	12	experiments	experiment	NOUN
ijassa-899	473	13	.	.	PUNCT
ijassa-899	474	1	their	their	PRON
ijassa-899	474	2	values	value	NOUN
ijassa-899	474	3	are	be	AUX
ijassa-899	474	4	given	give	VERB
ijassa-899	474	5	in	in	ADP
ijassa-899	474	6	table	table	NOUN
ijassa-899	474	7	7.2	7.2	NUM
ijassa-899	474	8	.	.	PUNCT
ijassa-899	475	1	in	in	ADP
ijassa-899	475	2	each	each	PRON
ijassa-899	475	3	of	of	ADP
ijassa-899	475	4	three	three	NUM
ijassa-899	475	5	variants	variant	NOUN
ijassa-899	475	6	of	of	ADP
ijassa-899	475	7	experiments	experiment	NOUN
ijassa-899	475	8	,	,	PUNCT
ijassa-899	475	9	the	the	DET
ijassa-899	475	10	gas	gas	NOUN
ijassa-899	475	11	content	content	NOUN
ijassa-899	475	12	values	value	NOUN
ijassa-899	475	13	are	be	AUX
ijassa-899	475	14	varied	varied	ADJ
ijassa-899	475	15	between	between	ADP
ijassa-899	475	16	0	0	NUM
ijassa-899	475	17	and	and	CCONJ
ijassa-899	475	18	800	800	NUM
ijassa-899	475	19	m3	m3	PROPN
ijassa-899	475	20	/	/	SYM
ijassa-899	475	21	t.	t.	NOUN
ijassa-899	475	22	for	for	ADP
ijassa-899	475	23	each	each	DET
ijassa-899	475	24	value	value	NOUN
ijassa-899	475	25	,	,	PUNCT
ijassa-899	475	26	the	the	DET
ijassa-899	475	27	probabilistic	probabilistic	ADJ
ijassa-899	475	28	density	density	NOUN
ijassa-899	475	29	of	of	ADP
ijassa-899	475	30	the	the	DET
ijassa-899	475	31	sample	sample	NOUN
ijassa-899	475	32	(	(	PUNCT
ijassa-899	475	33	i.e.	i.e.	X
ijassa-899	475	34	,	,	PUNCT
ijassa-899	475	35	the	the	DET
ijassa-899	475	36	probability	probability	NOUN
ijassa-899	475	37	of	of	ADP
ijassa-899	475	38	belonging	belong	VERB
ijassa-899	475	39	to	to	ADP
ijassa-899	475	40	each	each	PRON
ijassa-899	475	41	of	of	ADP
ijassa-899	475	42	the	the	DET
ijassa-899	475	43	three	three	NUM
ijassa-899	475	44	clusters	cluster	NOUN
ijassa-899	475	45	)	)	PUNCT
ijassa-899	475	46	,	,	PUNCT
ijassa-899	475	47	the	the	DET
ijassa-899	475	48	expected	expect	VERB
ijassa-899	475	49	values	value	NOUN
ijassa-899	475	50	of	of	ADP
ijassa-899	475	51	saturation	saturation	NOUN
ijassa-899	475	52	pressure	pressure	NOUN
ijassa-899	475	53	,	,	PUNCT
ijassa-899	475	54	reservoir	reservoir	NOUN
ijassa-899	475	55	oil	oil	NOUN
ijassa-899	475	56	density	density	NOUN
ijassa-899	475	57	,	,	PUNCT
ijassa-899	475	58	and	and	CCONJ
ijassa-899	475	59	oil	oil	NOUN
ijassa-899	475	60	volume	volume	NOUN
ijassa-899	475	61	coefficient	coefficient	NOUN
ijassa-899	475	62	are	be	AUX
ijassa-899	475	63	computed	compute	VERB
ijassa-899	475	64	using	use	VERB
ijassa-899	475	65	a	a	DET
ijassa-899	475	66	mixture	mixture	NOUN
ijassa-899	475	67	of	of	ADP
ijassa-899	475	68	student	student	NOUN
ijassa-899	475	69	distributions	distribution	NOUN
ijassa-899	475	70	.	.	PUNCT
ijassa-899	476	1	the	the	DET
ijassa-899	476	2	sample	sample	NOUN
ijassa-899	476	3	density	density	NOUN
ijassa-899	476	4	depending	depend	VERB
ijassa-899	476	5	on	on	ADP
ijassa-899	476	6	the	the	DET
ijassa-899	476	7	gas	gas	NOUN
ijassa-899	476	8	content	content	NOUN
ijassa-899	476	9	for	for	ADP
ijassa-899	476	10	the	the	DET
ijassa-899	476	11	three	three	NUM
ijassa-899	476	12	variants	variant	NOUN
ijassa-899	476	13	under	under	ADP
ijassa-899	476	14	study	study	NOUN
ijassa-899	476	15	is	be	AUX
ijassa-899	476	16	given	give	VERB
ijassa-899	476	17	on	on	ADP
ijassa-899	476	18	figure	figure	NOUN
ijassa-899	476	19	7.4	7.4	NUM
ijassa-899	476	20	.	.	PUNCT
ijassa-899	477	1	the	the	DET
ijassa-899	477	2	black	black	ADJ
ijassa-899	477	3	dotted	dot	VERB
ijassa-899	477	4	line	line	NOUN
ijassa-899	477	5	shows	show	VERB
ijassa-899	477	6	the	the	DET
ijassa-899	477	7	anomaly	anomaly	NOUN
ijassa-899	477	8	threshold	threshold	NOUN
ijassa-899	477	9	.	.	PUNCT
ijassa-899	478	1	if	if	SCONJ
ijassa-899	478	2	the	the	DET
ijassa-899	478	3	density	density	NOUN
ijassa-899	478	4	is	be	AUX
ijassa-899	478	5	below	below	ADP
ijassa-899	478	6	the	the	DET
ijassa-899	478	7	threshold	threshold	NOUN
ijassa-899	478	8	,	,	PUNCT
ijassa-899	478	9	the	the	DET
ijassa-899	478	10	sample	sample	NOUN
ijassa-899	478	11	is	be	AUX
ijassa-899	478	12	considered	consider	VERB
ijassa-899	478	13	abnormal	abnormal	ADJ
ijassa-899	478	14	.	.	PUNCT
ijassa-899	479	1	the	the	DET
ijassa-899	479	2	graph	graph	NOUN
ijassa-899	479	3	shows	show	VERB
ijassa-899	479	4	that	that	SCONJ
ijassa-899	479	5	in	in	ADP
ijassa-899	479	6	the	the	DET
ijassa-899	479	7	first	first	ADJ
ijassa-899	479	8	variant	variant	ADJ
ijassa-899	479	9	typical	typical	ADJ
ijassa-899	479	10	samples	sample	NOUN
ijassa-899	479	11	correspond	correspond	VERB
ijassa-899	479	12	to	to	ADP
ijassa-899	479	13	gas	gas	NOUN
ijassa-899	479	14	content	content	NOUN
ijassa-899	479	15	values	value	NOUN
ijassa-899	479	16	from	from	ADP
ijassa-899	479	17	90	90	NUM
ijassa-899	479	18	to	to	PART
ijassa-899	479	19	190	190	NUM
ijassa-899	479	20	m3	m3	PROPN
ijassa-899	479	21	/	/	SYM
ijassa-899	479	22	t	t	PROPN
ijassa-899	479	23	,	,	PUNCT
ijassa-899	479	24	in	in	ADP
ijassa-899	479	25	the	the	DET
ijassa-899	479	26	second	second	ADJ
ijassa-899	479	27	variant	variant	NOUN
ijassa-899	479	28	from	from	ADP
ijassa-899	479	29	0	0	NUM
ijassa-899	479	30	to	to	ADP
ijassa-899	479	31	110	110	NUM
ijassa-899	479	32	m3	m3	PROPN
ijassa-899	479	33	/	/	SYM
ijassa-899	479	34	t	t	PROPN
ijassa-899	479	35	,	,	PUNCT
ijassa-899	479	36	in	in	ADP
ijassa-899	479	37	the	the	DET
ijassa-899	479	38	third	third	NOUN
ijassa-899	479	39	from	from	ADP
ijassa-899	479	40	0	0	NUM
ijassa-899	479	41	to	to	ADP
ijassa-899	479	42	90	90	NUM
ijassa-899	479	43	m3	m3	PROPN
ijassa-899	479	44	/	/	SYM
ijassa-899	479	45	t.	t.	NOUN
ijassa-899	479	46	copyright	copyright	NOUN
ijassa-899	479	47	©	©	PROPN
ijassa-899	479	48	2020	2020	NUM
ijassa-899	479	49	assa	assa	NOUN
ijassa-899	479	50	.	.	PUNCT
ijassa-899	480	1	adv	adv	PROPN
ijassa-899	480	2	syst	syst	PROPN
ijassa-899	480	3	sci	sci	PROPN
ijassa-899	480	4	appl	appl	PROPN
ijassa-899	480	5	(	(	PUNCT
ijassa-899	480	6	2020	2020	NUM
ijassa-899	480	7	)	)	PUNCT
ijassa-899	480	8	114	114	NUM
ijassa-899	480	9	n.a	n.a	PROPN
ijassa-899	480	10	.	.	PROPN
ijassa-899	480	11	volkov	volkov	PROPN
ijassa-899	480	12	,	,	PUNCT
ijassa-899	480	13	e.yu	e.yu	PROPN
ijassa-899	480	14	.	.	PROPN
ijassa-899	480	15	dakhova	dakhova	PROPN
ijassa-899	480	16	,	,	PUNCT
ijassa-899	480	17	s.a	s.a	PROPN
ijassa-899	480	18	.	.	PROPN
ijassa-899	480	19	budennyy	budennyy	PROPN
ijassa-899	480	20	,	,	PUNCT
ijassa-899	480	21	a.m.	a.m.	PROPN
ijassa-899	480	22	andrianova	andrianova	PROPN
ijassa-899	480	23	fig	fig	PROPN
ijassa-899	480	24	.	.	PUNCT
ijassa-899	481	1	6.3	6.3	NUM
ijassa-899	481	2	.	.	PUNCT
ijassa-899	482	1	data	datum	NOUN
ijassa-899	482	2	visualization	visualization	NOUN
ijassa-899	482	3	and	and	CCONJ
ijassa-899	482	4	results	result	NOUN
ijassa-899	482	5	of	of	ADP
ijassa-899	482	6	application	application	NOUN
ijassa-899	482	7	of	of	ADP
ijassa-899	482	8	the	the	DET
ijassa-899	482	9	student	student	NOUN
ijassa-899	482	10	mixture	mixture	NOUN
ijassa-899	482	11	model	model	NOUN
ijassa-899	482	12	.	.	PUNCT
ijassa-899	483	1	the	the	DET
ijassa-899	483	2	plots	plot	NOUN
ijassa-899	483	3	above	above	ADP
ijassa-899	483	4	the	the	DET
ijassa-899	483	5	diagonal	diagonal	ADJ
ijassa-899	483	6	show	show	NOUN
ijassa-899	483	7	the	the	DET
ijassa-899	483	8	density	density	NOUN
ijassa-899	483	9	levels	level	NOUN
ijassa-899	483	10	of	of	ADP
ijassa-899	483	11	joint	joint	ADJ
ijassa-899	483	12	distribution	distribution	NOUN
ijassa-899	483	13	of	of	ADP
ijassa-899	483	14	features	feature	NOUN
ijassa-899	483	15	projected	project	VERB
ijassa-899	483	16	on	on	ADP
ijassa-899	483	17	subspace	subspace	NOUN
ijassa-899	483	18	of	of	ADP
ijassa-899	483	19	every	every	DET
ijassa-899	483	20	two	two	NUM
ijassa-899	483	21	features	feature	NOUN
ijassa-899	483	22	.	.	PUNCT
ijassa-899	484	1	the	the	DET
ijassa-899	484	2	diagonal	diagonal	ADJ
ijassa-899	484	3	plots	plot	NOUN
ijassa-899	484	4	show	show	VERB
ijassa-899	484	5	the	the	DET
ijassa-899	484	6	density	density	NOUN
ijassa-899	484	7	of	of	ADP
ijassa-899	484	8	every	every	DET
ijassa-899	484	9	feature	feature	NOUN
ijassa-899	484	10	.	.	PUNCT
ijassa-899	485	1	the	the	DET
ijassa-899	485	2	plots	plot	NOUN
ijassa-899	485	3	under	under	ADP
ijassa-899	485	4	the	the	DET
ijassa-899	485	5	diagonal	diagonal	ADJ
ijassa-899	485	6	show	show	NOUN
ijassa-899	485	7	the	the	DET
ijassa-899	485	8	clusterization	clusterization	NOUN
ijassa-899	485	9	based	base	VERB
ijassa-899	485	10	on	on	ADP
ijassa-899	485	11	the	the	DET
ijassa-899	485	12	application	application	NOUN
ijassa-899	485	13	of	of	ADP
ijassa-899	485	14	the	the	DET
ijassa-899	485	15	student	student	NOUN
ijassa-899	485	16	mixture	mixture	NOUN
ijassa-899	485	17	model	model	NOUN
ijassa-899	485	18	.	.	PUNCT
ijassa-899	486	1	every	every	DET
ijassa-899	486	2	ellipse	ellipse	NOUN
ijassa-899	486	3	corresponds	correspond	VERB
ijassa-899	486	4	to	to	ADP
ijassa-899	486	5	one	one	NUM
ijassa-899	486	6	of	of	ADP
ijassa-899	486	7	four	four	NUM
ijassa-899	486	8	clusters	cluster	NOUN
ijassa-899	486	9	/	/	SYM
ijassa-899	486	10	components	component	NOUN
ijassa-899	486	11	of	of	ADP
ijassa-899	486	12	the	the	DET
ijassa-899	486	13	mixture	mixture	NOUN
ijassa-899	486	14	the	the	DET
ijassa-899	486	15	figure	figure	NOUN
ijassa-899	486	16	7.5	7.5	NUM
ijassa-899	486	17	shows	show	VERB
ijassa-899	486	18	the	the	DET
ijassa-899	486	19	probability	probability	NOUN
ijassa-899	486	20	estimations	estimation	NOUN
ijassa-899	486	21	that	that	SCONJ
ijassa-899	486	22	the	the	DET
ijassa-899	486	23	sample	sample	NOUN
ijassa-899	486	24	corresponds	correspond	VERB
ijassa-899	486	25	to	to	ADP
ijassa-899	486	26	each	each	PRON
ijassa-899	486	27	of	of	ADP
ijassa-899	486	28	four	four	NUM
ijassa-899	486	29	clusters	cluster	NOUN
ijassa-899	486	30	for	for	ADP
ijassa-899	486	31	three	three	NUM
ijassa-899	486	32	variants	variant	NOUN
ijassa-899	486	33	of	of	ADP
ijassa-899	486	34	experiments	experiment	NOUN
ijassa-899	486	35	.	.	PUNCT
ijassa-899	487	1	for	for	ADP
ijassa-899	487	2	example	example	NOUN
ijassa-899	487	3	,	,	PUNCT
ijassa-899	487	4	considering	consider	VERB
ijassa-899	487	5	the	the	DET
ijassa-899	487	6	first	first	ADJ
ijassa-899	487	7	variant	variant	NOUN
ijassa-899	487	8	of	of	ADP
ijassa-899	487	9	experiments	experiment	NOUN
ijassa-899	487	10	,	,	PUNCT
ijassa-899	487	11	we	we	PRON
ijassa-899	487	12	can	can	AUX
ijassa-899	487	13	conclude	conclude	VERB
ijassa-899	487	14	the	the	DET
ijassa-899	487	15	sample	sample	NOUN
ijassa-899	487	16	belongs	belong	VERB
ijassa-899	487	17	to	to	ADP
ijassa-899	487	18	a	a	DET
ijassa-899	487	19	blue	blue	ADJ
ijassa-899	487	20	or	or	CCONJ
ijassa-899	487	21	green	green	ADJ
ijassa-899	487	22	cluster	cluster	NOUN
ijassa-899	487	23	depending	depend	VERB
ijassa-899	487	24	on	on	ADP
ijassa-899	487	25	the	the	DET
ijassa-899	487	26	value	value	NOUN
ijassa-899	487	27	of	of	ADP
ijassa-899	487	28	the	the	DET
ijassa-899	487	29	gas	gas	NOUN
ijassa-899	487	30	content	content	NOUN
ijassa-899	487	31	.	.	PUNCT
ijassa-899	488	1	figure	figure	VERB
ijassa-899	488	2	7.6	7.6	NUM
ijassa-899	488	3	contains	contain	VERB
ijassa-899	488	4	the	the	DET
ijassa-899	488	5	graphs	graph	NOUN
ijassa-899	488	6	of	of	ADP
ijassa-899	488	7	saturation	saturation	NOUN
ijassa-899	488	8	pressure	pressure	NOUN
ijassa-899	488	9	predictions	prediction	NOUN
ijassa-899	488	10	,	,	PUNCT
ijassa-899	488	11	reservoir	reservoir	NOUN
ijassa-899	488	12	density	density	NOUN
ijassa-899	488	13	of	of	ADP
ijassa-899	488	14	oil	oil	NOUN
ijassa-899	488	15	,	,	PUNCT
ijassa-899	488	16	and	and	CCONJ
ijassa-899	488	17	volume	volume	NOUN
ijassa-899	488	18	coefficient	coefficient	NOUN
ijassa-899	488	19	of	of	ADP
ijassa-899	488	20	oil	oil	NOUN
ijassa-899	488	21	in	in	ADP
ijassa-899	488	22	the	the	DET
ijassa-899	488	23	three	three	NUM
ijassa-899	488	24	above	above	ADP
ijassa-899	488	25	variants	variant	NOUN
ijassa-899	488	26	depending	depend	VERB
ijassa-899	488	27	on	on	ADP
ijassa-899	488	28	the	the	DET
ijassa-899	488	29	gas	gas	NOUN
ijassa-899	488	30	content	content	NOUN
ijassa-899	488	31	.	.	PUNCT
ijassa-899	489	1	orange	orange	PROPN
ijassa-899	489	2	dots	dots	PROPN
ijassa-899	489	3	corresponds	correspond	VERB
ijassa-899	489	4	the	the	DET
ijassa-899	489	5	samples	sample	NOUN
ijassa-899	489	6	used	use	VERB
ijassa-899	489	7	in	in	ADP
ijassa-899	489	8	train	train	NOUN
ijassa-899	489	9	data	datum	NOUN
ijassa-899	489	10	.	.	PUNCT
ijassa-899	490	1	the	the	DET
ijassa-899	490	2	shaded	shaded	ADJ
ijassa-899	490	3	area	area	NOUN
ijassa-899	490	4	denote	denote	VERB
ijassa-899	490	5	the	the	DET
ijassa-899	490	6	predictive	predictive	ADJ
ijassa-899	490	7	interval	interval	NOUN
ijassa-899	490	8	.	.	PUNCT
ijassa-899	491	1	the	the	DET
ijassa-899	491	2	less	less	ADV
ijassa-899	491	3	typical	typical	ADJ
ijassa-899	491	4	the	the	DET
ijassa-899	491	5	sample	sample	NOUN
ijassa-899	491	6	,	,	PUNCT
ijassa-899	491	7	the	the	PRON
ijassa-899	491	8	greater	great	ADJ
ijassa-899	491	9	the	the	DET
ijassa-899	491	10	uncertainty	uncertainty	NOUN
ijassa-899	491	11	of	of	ADP
ijassa-899	491	12	the	the	DET
ijassa-899	491	13	model	model	NOUN
ijassa-899	491	14	and	and	CCONJ
ijassa-899	491	15	the	the	DET
ijassa-899	491	16	confident	confident	ADJ
ijassa-899	491	17	interval	interval	NOUN
ijassa-899	491	18	is	be	AUX
ijassa-899	491	19	wider	wide	ADJ
ijassa-899	491	20	.	.	PUNCT
ijassa-899	492	1	note	note	VERB
ijassa-899	492	2	the	the	DET
ijassa-899	492	3	trajectories	trajectory	NOUN
ijassa-899	492	4	of	of	ADP
ijassa-899	492	5	the	the	DET
ijassa-899	492	6	predicted	predict	VERB
ijassa-899	492	7	values	value	NOUN
ijassa-899	492	8	are	be	AUX
ijassa-899	492	9	smooth	smooth	ADJ
ijassa-899	492	10	that	that	PRON
ijassa-899	492	11	follows	follow	VERB
ijassa-899	492	12	from	from	ADP
ijassa-899	492	13	the	the	DET
ijassa-899	492	14	model	model	NOUN
ijassa-899	492	15	construction	construction	NOUN
ijassa-899	492	16	.	.	PUNCT
ijassa-899	493	1	it	it	PRON
ijassa-899	493	2	is	be	AUX
ijassa-899	493	3	also	also	ADV
ijassa-899	493	4	worth	worth	ADJ
ijassa-899	493	5	noting	note	VERB
ijassa-899	493	6	that	that	SCONJ
ijassa-899	493	7	the	the	DET
ijassa-899	493	8	observed	observed	ADJ
ijassa-899	493	9	shift	shift	NOUN
ijassa-899	493	10	in	in	ADP
ijassa-899	493	11	predictions	prediction	NOUN
ijassa-899	493	12	relative	relative	ADJ
ijassa-899	493	13	copyright	copyright	NOUN
ijassa-899	493	14	©	©	PROPN
ijassa-899	493	15	2020	2020	NUM
ijassa-899	493	16	assa	assa	NOUN
ijassa-899	493	17	.	.	PUNCT
ijassa-899	494	1	adv	adv	PROPN
ijassa-899	494	2	syst	syst	PROPN
ijassa-899	494	3	sci	sci	PROPN
ijassa-899	494	4	appl	appl	PROPN
ijassa-899	494	5	(	(	PUNCT
ijassa-899	494	6	2020	2020	NUM
ijassa-899	494	7	)	)	PUNCT
ijassa-899	494	8	student	student	NOUN
ijassa-899	494	9	mixture	mixture	NOUN
ijassa-899	494	10	and	and	CCONJ
ijassa-899	494	11	its	its	PRON
ijassa-899	494	12	machine	machine	NOUN
ijassa-899	494	13	learning	learn	VERB
ijassa-899	494	14	applications	application	NOUN
ijassa-899	494	15	to	to	ADP
ijassa-899	494	16	pvt	pvt	PROPN
ijassa-899	494	17	properties	property	NOUN
ijassa-899	494	18	115	115	NUM
ijassa-899	494	19	table	table	NOUN
ijassa-899	494	20	7.2	7.2	NUM
ijassa-899	494	21	.	.	PUNCT
ijassa-899	495	1	fixed	fix	VERB
ijassa-899	495	2	values	value	NOUN
ijassa-899	495	3	of	of	ADP
ijassa-899	495	4	features	feature	NOUN
ijassa-899	495	5	in	in	ADP
ijassa-899	495	6	model	model	NOUN
ijassa-899	495	7	experiments	experiment	NOUN
ijassa-899	495	8	feature	feature	VERB
ijassa-899	495	9	variant	variant	NOUN
ijassa-899	495	10	1	1	NUM
ijassa-899	495	11	variant	variant	NOUN
ijassa-899	495	12	2	2	NUM
ijassa-899	495	13	variant	variant	NOUN
ijassa-899	495	14	3	3	NUM
ijassa-899	495	15	reservoir	reservoir	NOUN
ijassa-899	495	16	pressure	pressure	NOUN
ijassa-899	495	17	,	,	PUNCT
ijassa-899	495	18	mpa	mpa	PROPN
ijassa-899	495	19	35	35	NUM
ijassa-899	495	20	25	25	NUM
ijassa-899	495	21	18	18	NUM
ijassa-899	495	22	reservoir	reservoir	NOUN
ijassa-899	495	23	temperature	temperature	NOUN
ijassa-899	495	24	,	,	PUNCT
ijassa-899	495	25	oc	oc	VERB
ijassa-899	495	26	100	100	NUM
ijassa-899	495	27	75	75	NUM
ijassa-899	495	28	50	50	NUM
ijassa-899	495	29	surface	surface	NOUN
ijassa-899	495	30	gas	gas	NOUN
ijassa-899	495	31	density	density	NOUN
ijassa-899	495	32	,	,	PUNCT
ijassa-899	495	33	kg	kg	PROPN
ijassa-899	495	34	/	/	SYM
ijassa-899	495	35	m3	m3	PROPN
ijassa-899	495	36	1.2	1.2	NUM
ijassa-899	495	37	0.95	0.95	NUM
ijassa-899	495	38	0.68	0.68	NUM
ijassa-899	495	39	surface	surface	NOUN
ijassa-899	495	40	oil	oil	NOUN
ijassa-899	495	41	density	density	NOUN
ijassa-899	495	42	,	,	PUNCT
ijassa-899	495	43	kg	kg	PROPN
ijassa-899	495	44	/	/	SYM
ijassa-899	495	45	m3	m3	PROPN
ijassa-899	495	46	800	800	NUM
ijassa-899	495	47	845	845	NUM
ijassa-899	495	48	880	880	NUM
ijassa-899	495	49	reservoir	reservoir	NOUN
ijassa-899	495	50	oil	oil	NOUN
ijassa-899	495	51	viscosity	viscosity	NOUN
ijassa-899	495	52	,	,	PUNCT
ijassa-899	495	53	mpa	mpa	PROPN
ijassa-899	495	54	*	*	PUNCT
ijassa-899	495	55	s	s	PROPN
ijassa-899	495	56	0.5	0.5	NUM
ijassa-899	495	57	2	2	NUM
ijassa-899	495	58	5	5	NUM
ijassa-899	495	59	gas	gas	NOUN
ijassa-899	495	60	content	content	NOUN
ijassa-899	495	61	,	,	PUNCT
ijassa-899	495	62	m3	m3	PROPN
ijassa-899	495	63	/	/	SYM
ijassa-899	495	64	t	t	PROPN
ijassa-899	495	65	0	0	NUM
ijassa-899	495	66	.	.	PUNCT
ijassa-899	495	67	.	.	PUNCT
ijassa-899	496	1	.	.	PUNCT
ijassa-899	497	1	800	800	NUM
ijassa-899	497	2	0	0	NUM
ijassa-899	497	3	.	.	PUNCT
ijassa-899	497	4	.	.	PUNCT
ijassa-899	497	5	.	.	PUNCT
ijassa-899	498	1	800	800	NUM
ijassa-899	498	2	0	0	NUM
ijassa-899	498	3	.	.	PUNCT
ijassa-899	498	4	.	.	PUNCT
ijassa-899	499	1	.	.	PUNCT
ijassa-899	500	1	800	800	NUM
ijassa-899	500	2	saturation	saturation	NOUN
ijassa-899	500	3	pressure	pressure	NOUN
ijassa-899	500	4	,	,	PUNCT
ijassa-899	500	5	mpa	mpa	PROPN
ijassa-899	500	6	?	?	PUNCT
ijassa-899	500	7	?	?	PUNCT
ijassa-899	500	8	?	?	PUNCT
ijassa-899	501	1	oil	oil	NOUN
ijassa-899	501	2	reservoir	reservoir	NOUN
ijassa-899	501	3	density	density	NOUN
ijassa-899	501	4	,	,	PUNCT
ijassa-899	501	5	kg	kg	PROPN
ijassa-899	501	6	/	/	SYM
ijassa-899	501	7	m3	m3	PROPN
ijassa-899	501	8	?	?	PUNCT
ijassa-899	501	9	?	?	PUNCT
ijassa-899	501	10	?	?	PUNCT
ijassa-899	502	1	volume	volume	NOUN
ijassa-899	502	2	coefficient	coefficient	NOUN
ijassa-899	502	3	.	.	PUNCT
ijassa-899	503	1	oil	oil	NOUN
ijassa-899	503	2	,	,	PUNCT
ijassa-899	503	3	m3	m3	PROPN
ijassa-899	503	4	/	/	SYM
ijassa-899	503	5	m3	m3	PROPN
ijassa-899	503	6	?	?	PUNCT
ijassa-899	503	7	?	?	PUNCT
ijassa-899	503	8	?	?	PUNCT
ijassa-899	504	1	fig	fig	NOUN
ijassa-899	504	2	.	.	PUNCT
ijassa-899	505	1	7.4	7.4	NUM
ijassa-899	505	2	.	.	PUNCT
ijassa-899	506	1	sample	sample	NOUN
ijassa-899	506	2	density	density	NOUN
ijassa-899	506	3	depending	depend	VERB
ijassa-899	506	4	on	on	ADP
ijassa-899	506	5	the	the	DET
ijassa-899	506	6	gas	gas	NOUN
ijassa-899	506	7	content	content	NOUN
ijassa-899	506	8	for	for	ADP
ijassa-899	506	9	three	three	NUM
ijassa-899	506	10	variants	variant	NOUN
ijassa-899	506	11	of	of	ADP
ijassa-899	506	12	experiments	experiment	NOUN
ijassa-899	506	13	to	to	ADP
ijassa-899	506	14	the	the	DET
ijassa-899	506	15	total	total	ADJ
ijassa-899	506	16	mass	mass	NOUN
ijassa-899	506	17	of	of	ADP
ijassa-899	506	18	points	point	NOUN
ijassa-899	506	19	is	be	AUX
ijassa-899	506	20	due	due	ADJ
ijassa-899	506	21	to	to	ADP
ijassa-899	506	22	the	the	DET
ijassa-899	506	23	fact	fact	NOUN
ijassa-899	506	24	that	that	SCONJ
ijassa-899	506	25	each	each	DET
ijassa-899	506	26	variant	variant	NOUN
ijassa-899	506	27	of	of	ADP
ijassa-899	506	28	experiments	experiment	NOUN
ijassa-899	506	29	has	have	AUX
ijassa-899	506	30	fixed	fix	VERB
ijassa-899	506	31	features	feature	NOUN
ijassa-899	506	32	not	not	PART
ijassa-899	506	33	presented	present	VERB
ijassa-899	506	34	on	on	ADP
ijassa-899	506	35	the	the	DET
ijassa-899	506	36	charts	chart	NOUN
ijassa-899	506	37	.	.	PUNCT
ijassa-899	507	1	if	if	SCONJ
ijassa-899	507	2	there	there	PRON
ijassa-899	507	3	were	be	VERB
ijassa-899	507	4	n’t	not	PART
ijassa-899	507	5	fixed	fix	VERB
ijassa-899	507	6	features	feature	VERB
ijassa-899	507	7	the	the	DET
ijassa-899	507	8	predictions	prediction	NOUN
ijassa-899	507	9	would	would	AUX
ijassa-899	507	10	look	look	VERB
ijassa-899	507	11	like	like	ADP
ijassa-899	507	12	averages	average	NOUN
ijassa-899	507	13	.	.	PUNCT
ijassa-899	508	1	fig	fig	NOUN
ijassa-899	508	2	.	.	PUNCT
ijassa-899	509	1	7.5	7.5	NUM
ijassa-899	509	2	.	.	PUNCT
ijassa-899	510	1	probability	probability	NOUN
ijassa-899	510	2	estimation	estimation	NOUN
ijassa-899	510	3	of	of	ADP
ijassa-899	510	4	the	the	DET
ijassa-899	510	5	sample	sample	NOUN
ijassa-899	510	6	belonging	belong	VERB
ijassa-899	510	7	to	to	ADP
ijassa-899	510	8	each	each	PRON
ijassa-899	510	9	of	of	ADP
ijassa-899	510	10	four	four	NUM
ijassa-899	510	11	clusters	cluster	NOUN
ijassa-899	510	12	depending	depend	VERB
ijassa-899	510	13	on	on	ADP
ijassa-899	510	14	the	the	DET
ijassa-899	510	15	gas	gas	NOUN
ijassa-899	510	16	content	content	NOUN
ijassa-899	510	17	7.2	7.2	NUM
ijassa-899	510	18	.	.	PUNCT
ijassa-899	511	1	predictions	prediction	NOUN
ijassa-899	511	2	quality	quality	VERB
ijassa-899	511	3	the	the	DET
ijassa-899	511	4	developed	develop	VERB
ijassa-899	511	5	model	model	NOUN
ijassa-899	511	6	calculates	calculate	VERB
ijassa-899	511	7	the	the	DET
ijassa-899	511	8	expected	expect	VERB
ijassa-899	511	9	values	value	NOUN
ijassa-899	511	10	for	for	ADP
ijassa-899	511	11	unknown	unknown	ADJ
ijassa-899	511	12	feature	feature	NOUN
ijassa-899	511	13	values	value	NOUN
ijassa-899	511	14	based	base	VERB
ijassa-899	511	15	on	on	ADP
ijassa-899	511	16	the	the	DET
ijassa-899	511	17	entered	enter	VERB
ijassa-899	511	18	sample	sample	NOUN
ijassa-899	511	19	.	.	PUNCT
ijassa-899	512	1	the	the	DET
ijassa-899	512	2	degree	degree	NOUN
ijassa-899	512	3	of	of	ADP
ijassa-899	512	4	deviation	deviation	NOUN
ijassa-899	512	5	of	of	ADP
ijassa-899	512	6	the	the	DET
ijassa-899	512	7	estimated	estimate	VERB
ijassa-899	512	8	values	value	NOUN
ijassa-899	512	9	from	from	ADP
ijassa-899	512	10	the	the	DET
ijassa-899	512	11	true	true	ADJ
ijassa-899	512	12	values	value	NOUN
ijassa-899	512	13	is	be	AUX
ijassa-899	512	14	estimated	estimate	VERB
ijassa-899	512	15	using	use	VERB
ijassa-899	512	16	quality	quality	NOUN
ijassa-899	512	17	metrics	metric	NOUN
ijassa-899	512	18	.	.	PUNCT
ijassa-899	513	1	let	let	VERB
ijassa-899	513	2	xi	xi	PRON
ijassa-899	513	3	be	be	AUX
ijassa-899	513	4	a	a	DET
ijassa-899	513	5	true	true	ADJ
ijassa-899	513	6	value	value	NOUN
ijassa-899	513	7	of	of	ADP
ijassa-899	513	8	the	the	DET
ijassa-899	513	9	feature	feature	NOUN
ijassa-899	513	10	,	,	PUNCT
ijassa-899	513	11	and	and	CCONJ
ijassa-899	513	12	x̂i	x̂i	NUM
ijassa-899	513	13	be	be	AUX
ijassa-899	513	14	a	a	DET
ijassa-899	513	15	predicted	predict	VERB
ijassa-899	513	16	value	value	NOUN
ijassa-899	513	17	under	under	ADP
ijassa-899	513	18	the	the	DET
ijassa-899	513	19	assumption	assumption	NOUN
ijassa-899	513	20	that	that	SCONJ
ijassa-899	513	21	xi	xi	PROPN
ijassa-899	513	22	is	be	AUX
ijassa-899	513	23	unknown	unknown	ADJ
ijassa-899	513	24	.	.	PUNCT
ijassa-899	514	1	then	then	ADV
ijassa-899	514	2	the	the	DET
ijassa-899	514	3	relative	relative	ADJ
ijassa-899	514	4	error	error	NOUN
ijassa-899	514	5	of	of	ADP
ijassa-899	514	6	the	the	DET
ijassa-899	514	7	prediction	prediction	NOUN
ijassa-899	514	8	xi	xi	X
ijassa-899	514	9	is	be	AUX
ijassa-899	514	10	ei	ei	X
ijassa-899	514	11	=	=	PUNCT
ijassa-899	514	12	(	(	PUNCT
ijassa-899	514	13	xi	xi	X
ijassa-899	514	14	−	−	PROPN
ijassa-899	514	15	x̂i)/xi	x̂i)/xi	PROPN
ijassa-899	514	16	.	.	PUNCT
ijassa-899	515	1	it	it	PRON
ijassa-899	515	2	measures	measure	VERB
ijassa-899	515	3	the	the	DET
ijassa-899	515	4	deviation	deviation	NOUN
ijassa-899	515	5	of	of	ADP
ijassa-899	515	6	the	the	DET
ijassa-899	515	7	predicted	predict	VERB
ijassa-899	515	8	value	value	NOUN
ijassa-899	515	9	from	from	ADP
ijassa-899	515	10	the	the	DET
ijassa-899	515	11	true	true	ADJ
ijassa-899	515	12	value	value	NOUN
ijassa-899	515	13	in	in	ADP
ijassa-899	515	14	relation	relation	NOUN
ijassa-899	515	15	to	to	ADP
ijassa-899	515	16	the	the	DET
ijassa-899	515	17	actual	actual	ADJ
ijassa-899	515	18	value	value	NOUN
ijassa-899	515	19	of	of	ADP
ijassa-899	515	20	the	the	DET
ijassa-899	515	21	true	true	ADJ
ijassa-899	515	22	value	value	NOUN
ijassa-899	515	23	.	.	PUNCT
ijassa-899	516	1	prediction	prediction	NOUN
ijassa-899	516	2	errors	error	NOUN
ijassa-899	516	3	are	be	AUX
ijassa-899	516	4	calculated	calculate	VERB
ijassa-899	516	5	for	for	ADP
ijassa-899	516	6	a	a	DET
ijassa-899	516	7	test	test	NOUN
ijassa-899	516	8	set	set	NOUN
ijassa-899	516	9	containing	contain	VERB
ijassa-899	516	10	about	about	ADV
ijassa-899	516	11	650	650	NUM
ijassa-899	516	12	samples	sample	NOUN
ijassa-899	516	13	that	that	PRON
ijassa-899	516	14	are	be	AUX
ijassa-899	516	15	not	not	PART
ijassa-899	516	16	involved	involve	VERB
ijassa-899	516	17	in	in	ADP
ijassa-899	516	18	building	build	VERB
ijassa-899	516	19	the	the	DET
ijassa-899	516	20	model	model	NOUN
ijassa-899	516	21	.	.	PUNCT
ijassa-899	517	1	when	when	SCONJ
ijassa-899	517	2	predicting	predict	VERB
ijassa-899	517	3	each	each	DET
ijassa-899	517	4	value	value	NOUN
ijassa-899	517	5	of	of	ADP
ijassa-899	517	6	the	the	DET
ijassa-899	517	7	model	model	NOUN
ijassa-899	517	8	passed	pass	VERB
ijassa-899	517	9	all	all	DET
ijassa-899	517	10	the	the	DET
ijassa-899	517	11	known	know	VERB
ijassa-899	517	12	values	value	NOUN
ijassa-899	517	13	of	of	ADP
ijassa-899	517	14	the	the	DET
ijassa-899	517	15	samples	sample	NOUN
ijassa-899	517	16	in	in	ADP
ijassa-899	517	17	addition	addition	NOUN
ijassa-899	517	18	to	to	PART
ijassa-899	517	19	predicted	predict	VERB
ijassa-899	517	20	.	.	PUNCT
ijassa-899	518	1	the	the	DET
ijassa-899	518	2	prediction	prediction	NOUN
ijassa-899	518	3	quality	quality	NOUN
ijassa-899	518	4	is	be	AUX
ijassa-899	518	5	calculated	calculate	VERB
ijassa-899	518	6	as	as	ADP
ijassa-899	518	7	the	the	DET
ijassa-899	518	8	average	average	ADJ
ijassa-899	518	9	absolute	absolute	ADJ
ijassa-899	518	10	and	and	CCONJ
ijassa-899	518	11	standard	standard	ADJ
ijassa-899	518	12	error	error	NOUN
ijassa-899	518	13	in	in	ADP
ijassa-899	518	14	percentages	percentage	NOUN
ijassa-899	518	15	,	,	PUNCT
ijassa-899	518	16	copyright	copyright	NOUN
ijassa-899	518	17	©	©	PROPN
ijassa-899	518	18	2020	2020	NUM
ijassa-899	518	19	assa	assa	NOUN
ijassa-899	518	20	.	.	PUNCT
ijassa-899	519	1	adv	adv	PROPN
ijassa-899	519	2	syst	syst	PROPN
ijassa-899	519	3	sci	sci	PROPN
ijassa-899	519	4	appl	appl	PROPN
ijassa-899	519	5	(	(	PUNCT
ijassa-899	519	6	2020	2020	NUM
ijassa-899	519	7	)	)	PUNCT
ijassa-899	519	8	116	116	NUM
ijassa-899	519	9	n.a	n.a	PROPN
ijassa-899	519	10	.	.	PROPN
ijassa-899	519	11	volkov	volkov	PROPN
ijassa-899	519	12	,	,	PUNCT
ijassa-899	519	13	e.yu	e.yu	PROPN
ijassa-899	519	14	.	.	PROPN
ijassa-899	519	15	dakhova	dakhova	PROPN
ijassa-899	519	16	,	,	PUNCT
ijassa-899	519	17	s.a	s.a	PROPN
ijassa-899	519	18	.	.	PROPN
ijassa-899	519	19	budennyy	budennyy	PROPN
ijassa-899	519	20	,	,	PUNCT
ijassa-899	519	21	a.m.	a.m.	PROPN
ijassa-899	519	22	andrianova	andrianova	PROPN
ijassa-899	519	23	fig	fig	PROPN
ijassa-899	519	24	.	.	PUNCT
ijassa-899	520	1	7.6	7.6	NUM
ijassa-899	520	2	.	.	PUNCT
ijassa-899	520	3	predictions	prediction	NOUN
ijassa-899	520	4	of	of	ADP
ijassa-899	520	5	three	three	NUM
ijassa-899	520	6	features	feature	NOUN
ijassa-899	520	7	depending	depend	VERB
ijassa-899	520	8	on	on	ADP
ijassa-899	520	9	gas	gas	NOUN
ijassa-899	520	10	content	content	NOUN
ijassa-899	520	11	.	.	PUNCT
ijassa-899	521	1	shaded	shaded	ADJ
ijassa-899	521	2	area	area	NOUN
ijassa-899	521	3	denotes	denote	VERB
ijassa-899	521	4	confidence	confidence	NOUN
ijassa-899	521	5	interval	interval	NOUN
ijassa-899	521	6	which	which	PRON
ijassa-899	521	7	are	be	AUX
ijassa-899	521	8	defined	define	VERB
ijassa-899	521	9	as	as	ADP
ijassa-899	521	10	mape	mape	NOUN
ijassa-899	521	11	=	=	SYM
ijassa-899	521	12	100	100	NUM
ijassa-899	521	13	%	%	NOUN
ijassa-899	521	14	n	n	CCONJ
ijassa-899	521	15	n∑	n∑	PROPN
ijassa-899	521	16	i=1	i=1	PROPN
ijassa-899	521	17	|ei|	|ei|	PROPN
ijassa-899	521	18	,	,	PUNCT
ijassa-899	521	19	rmspe	rmspe	PROPN
ijassa-899	521	20	=	=	SYM
ijassa-899	521	21	100	100	NUM
ijassa-899	521	22	%	%	NOUN
ijassa-899	521	23	√√√√	√√√√	ADP
ijassa-899	521	24	1	1	NUM
ijassa-899	521	25	n	n	NUM
ijassa-899	521	26	n∑	n∑	NOUN
ijassa-899	521	27	i=1	i=1	PROPN
ijassa-899	522	1	e2	e2	PROPN
ijassa-899	522	2	i	i	PRON
ijassa-899	522	3	.	.	PUNCT
ijassa-899	523	1	for	for	ADP
ijassa-899	523	2	metrics	metric	NOUN
ijassa-899	523	3	evaluation	evaluation	NOUN
ijassa-899	523	4	,	,	PUNCT
ijassa-899	523	5	2.5	2.5	NUM
ijassa-899	523	6	%	%	NOUN
ijassa-899	523	7	of	of	ADP
ijassa-899	523	8	the	the	DET
ijassa-899	523	9	highest	high	ADJ
ijassa-899	523	10	and	and	CCONJ
ijassa-899	523	11	2.5	2.5	NUM
ijassa-899	523	12	%	%	NOUN
ijassa-899	523	13	of	of	ADP
ijassa-899	523	14	the	the	DET
ijassa-899	523	15	lowest	low	ADJ
ijassa-899	523	16	values	value	NOUN
ijassa-899	523	17	are	be	AUX
ijassa-899	523	18	excluded	exclude	VERB
ijassa-899	523	19	from	from	ADP
ijassa-899	523	20	the	the	DET
ijassa-899	523	21	set	set	NOUN
ijassa-899	523	22	of	of	ADP
ijassa-899	523	23	numbers	number	NOUN
ijassa-899	523	24	ei	ei	ADP
ijassa-899	523	25	due	due	ADP
ijassa-899	523	26	to	to	ADP
ijassa-899	523	27	outliers	outlier	NOUN
ijassa-899	523	28	and	and	CCONJ
ijassa-899	523	29	incorrect	incorrect	ADJ
ijassa-899	523	30	values	value	NOUN
ijassa-899	523	31	in	in	ADP
ijassa-899	523	32	the	the	DET
ijassa-899	523	33	source	source	NOUN
ijassa-899	523	34	data	datum	NOUN
ijassa-899	523	35	.	.	PUNCT
ijassa-899	524	1	for	for	ADP
ijassa-899	524	2	comparison	comparison	NOUN
ijassa-899	524	3	,	,	PUNCT
ijassa-899	524	4	9	9	NUM
ijassa-899	524	5	regression	regression	NOUN
ijassa-899	524	6	models	model	NOUN
ijassa-899	524	7	were	be	AUX
ijassa-899	524	8	built	build	VERB
ijassa-899	524	9	for	for	ADP
ijassa-899	524	10	each	each	DET
ijassa-899	524	11	feature	feature	NOUN
ijassa-899	524	12	for	for	ADP
ijassa-899	524	13	the	the	DET
ijassa-899	524	14	following	follow	VERB
ijassa-899	524	15	machine	machine	NOUN
ijassa-899	524	16	learning	learning	NOUN
ijassa-899	524	17	methods	method	NOUN
ijassa-899	524	18	:	:	PUNCT
ijassa-899	524	19	gradient	gradient	NOUN
ijassa-899	524	20	boosting	boosting	NOUN
ijassa-899	524	21	(	(	PUNCT
ijassa-899	524	22	xgboost†	xgboost†	ADJ
ijassa-899	524	23	,	,	PUNCT
ijassa-899	524	24	lgbm‡	lgbm‡	NOUN
ijassa-899	524	25	,	,	PUNCT
ijassa-899	524	26	catboost§	catboost§	NUM
ijassa-899	524	27	)	)	PUNCT
ijassa-899	524	28	,	,	PUNCT
ijassa-899	524	29	as	as	ADV
ijassa-899	524	30	well	well	ADV
ijassa-899	524	31	as	as	ADP
ijassa-899	524	32	sklearn	sklearn	ADJ
ijassa-899	524	33	implementations¶	implementations¶	NOUN
ijassa-899	524	34	of	of	ADP
ijassa-899	524	35	random	random	ADJ
ijassa-899	524	36	forest	forest	NOUN
ijassa-899	524	37	(	(	PUNCT
ijassa-899	524	38	rf	rf	NOUN
ijassa-899	524	39	)	)	PUNCT
ijassa-899	524	40	,	,	PUNCT
ijassa-899	524	41	svm	svm	PROPN
ijassa-899	524	42	regression	regression	NOUN
ijassa-899	524	43	,	,	PUNCT
ijassa-899	524	44	neural	neural	ADJ
ijassa-899	524	45	network	network	NOUN
ijassa-899	524	46	(	(	PUNCT
ijassa-899	524	47	ann	ann	PROPN
ijassa-899	524	48	)	)	PUNCT
ijassa-899	524	49	.	.	PUNCT
ijassa-899	525	1	each	each	DET
ijassa-899	525	2	such	such	ADJ
ijassa-899	525	3	model	model	NOUN
ijassa-899	525	4	is	be	AUX
ijassa-899	525	5	trained	train	VERB
ijassa-899	525	6	to	to	PART
ijassa-899	525	7	predict	predict	VERB
ijassa-899	525	8	one	one	NUM
ijassa-899	525	9	of	of	ADP
ijassa-899	525	10	the	the	DET
ijassa-899	525	11	features	feature	NOUN
ijassa-899	525	12	,	,	PUNCT
ijassa-899	525	13	considering	consider	VERB
ijassa-899	525	14	all	all	DET
ijassa-899	525	15	the	the	DET
ijassa-899	525	16	other	other	ADJ
ijassa-899	525	17	features	feature	NOUN
ijassa-899	525	18	as	as	ADP
ijassa-899	525	19	a	a	DET
ijassa-899	525	20	feature	feature	NOUN
ijassa-899	525	21	description	description	NOUN
ijassa-899	525	22	.	.	PUNCT
ijassa-899	526	1	optimal	optimal	ADJ
ijassa-899	526	2	hyperparameters	hyperparameter	NOUN
ijassa-899	526	3	for	for	ADP
ijassa-899	526	4	each	each	DET
ijassa-899	526	5	model	model	NOUN
ijassa-899	526	6	are	be	AUX
ijassa-899	526	7	selected	select	VERB
ijassa-899	526	8	using	use	VERB
ijassa-899	526	9	cross	cross	NOUN
ijassa-899	526	10	-	-	NOUN
ijassa-899	526	11	validation	validation	NOUN
ijassa-899	526	12	on	on	ADP
ijassa-899	526	13	the	the	DET
ijassa-899	526	14	training	training	NOUN
ijassa-899	526	15	set	set	NOUN
ijassa-899	526	16	.	.	PUNCT
ijassa-899	527	1	tables	table	NOUN
ijassa-899	527	2	7.3	7.3	NUM
ijassa-899	527	3	,	,	PUNCT
ijassa-899	527	4	7.4	7.4	NUM
ijassa-899	527	5	contain	contain	VERB
ijassa-899	527	6	the	the	DET
ijassa-899	527	7	values	value	NOUN
ijassa-899	527	8	of	of	ADP
ijassa-899	527	9	the	the	DET
ijassa-899	527	10	mape	mape	NOUN
ijassa-899	527	11	and	and	CCONJ
ijassa-899	527	12	rmspe	rmspe	ADJ
ijassa-899	527	13	metrics	metric	NOUN
ijassa-899	527	14	for	for	ADP
ijassa-899	527	15	the	the	DET
ijassa-899	527	16	test	test	NOUN
ijassa-899	527	17	dataset	dataset	NOUN
ijassa-899	527	18	.	.	PUNCT
ijassa-899	528	1	one	one	PRON
ijassa-899	528	2	can	can	AUX
ijassa-899	528	3	notice	notice	VERB
ijassa-899	528	4	in	in	ADP
ijassa-899	528	5	5	5	NUM
ijassa-899	528	6	out	out	ADP
ijassa-899	528	7	of	of	ADP
ijassa-899	528	8	9	9	NUM
ijassa-899	528	9	cases	case	NOUN
ijassa-899	528	10	the	the	DET
ijassa-899	528	11	model	model	NOUN
ijassa-899	528	12	prediction	prediction	NOUN
ijassa-899	528	13	of	of	ADP
ijassa-899	528	14	the	the	DET
ijassa-899	528	15	student	student	NOUN
ijassa-899	528	16	mixture	mixture	NOUN
ijassa-899	528	17	is	be	AUX
ijassa-899	528	18	more	more	ADV
ijassa-899	528	19	accurate	accurate	ADJ
ijassa-899	528	20	than	than	ADP
ijassa-899	528	21	all	all	DET
ijassa-899	528	22	the	the	DET
ijassa-899	528	23	other	other	ADJ
ijassa-899	528	24	models	model	NOUN
ijassa-899	528	25	,	,	PUNCT
ijassa-899	528	26	and	and	CCONJ
ijassa-899	528	27	in	in	ADP
ijassa-899	528	28	the	the	DET
ijassa-899	528	29	other	other	ADJ
ijassa-899	528	30	cases	case	NOUN
ijassa-899	528	31	it	it	PRON
ijassa-899	528	32	is	be	AUX
ijassa-899	528	33	not	not	PART
ijassa-899	528	34	far	far	ADV
ijassa-899	528	35	behind	behind	ADP
ijassa-899	528	36	them	they	PRON
ijassa-899	528	37	.	.	PUNCT
ijassa-899	529	1	moreover	moreover	ADV
ijassa-899	529	2	,	,	PUNCT
ijassa-899	529	3	experts	expert	NOUN
ijassa-899	529	4	usually	usually	ADV
ijassa-899	529	5	trust	trust	VERB
ijassa-899	529	6	reservoir	reservoir	NOUN
ijassa-899	529	7	pressure	pressure	NOUN
ijassa-899	529	8	and	and	CCONJ
ijassa-899	529	9	temperature	temperature	NOUN
ijassa-899	529	10	,	,	PUNCT
ijassa-899	529	11	which	which	PRON
ijassa-899	529	12	are	be	AUX
ijassa-899	529	13	more	more	ADV
ijassa-899	529	14	accurately	accurately	ADV
ijassa-899	529	15	predicted	predict	VERB
ijassa-899	529	16	by	by	ADP
ijassa-899	529	17	gradient	gradient	NOUN
ijassa-899	529	18	boosting	boosting	NOUN
ijassa-899	529	19	,	,	PUNCT
ijassa-899	529	20	and	and	CCONJ
ijassa-899	529	21	therefore	therefore	ADV
ijassa-899	529	22	their	their	PRON
ijassa-899	529	23	prediction	prediction	NOUN
ijassa-899	529	24	is	be	AUX
ijassa-899	529	25	not	not	PART
ijassa-899	529	26	very	very	ADV
ijassa-899	529	27	significant	significant	ADJ
ijassa-899	529	28	in	in	ADP
ijassa-899	529	29	this	this	DET
ijassa-899	529	30	task	task	NOUN
ijassa-899	529	31	.	.	PUNCT
ijassa-899	530	1	†https://xgboost.ai/	†https://xgboost.ai/	PROPN
ijassa-899	530	2	‡https://lightgbm.readthedocs.io/	‡https://lightgbm.readthedocs.io/	PROPN
ijassa-899	530	3	§	§	PROPN
ijassa-899	530	4	https://catboost.ai/	https://catboost.ai/	PROPN
ijassa-899	530	5	¶https://scikit	¶https://scikit	PROPN
ijassa-899	530	6	-	-	PUNCT
ijassa-899	530	7	learn.org/	learn.org/	ADJ
ijassa-899	530	8	copyright	copyright	NOUN
ijassa-899	530	9	©	©	PROPN
ijassa-899	530	10	2020	2020	NUM
ijassa-899	530	11	assa	assa	NOUN
ijassa-899	530	12	.	.	PUNCT
ijassa-899	531	1	adv	adv	PROPN
ijassa-899	531	2	syst	syst	PROPN
ijassa-899	531	3	sci	sci	PROPN
ijassa-899	531	4	appl	appl	PROPN
ijassa-899	531	5	(	(	PUNCT
ijassa-899	531	6	2020	2020	NUM
ijassa-899	531	7	)	)	PUNCT
ijassa-899	531	8	student	student	NOUN
ijassa-899	531	9	mixture	mixture	NOUN
ijassa-899	531	10	and	and	CCONJ
ijassa-899	531	11	its	its	PRON
ijassa-899	531	12	machine	machine	NOUN
ijassa-899	531	13	learning	learn	VERB
ijassa-899	531	14	applications	application	NOUN
ijassa-899	531	15	to	to	ADP
ijassa-899	531	16	pvt	pvt	PROPN
ijassa-899	531	17	properties	property	NOUN
ijassa-899	531	18	117	117	NUM
ijassa-899	531	19	table	table	NOUN
ijassa-899	531	20	7.3	7.3	NUM
ijassa-899	531	21	.	.	PUNCT
ijassa-899	532	1	comparison	comparison	NOUN
ijassa-899	532	2	of	of	ADP
ijassa-899	532	3	prediction	prediction	NOUN
ijassa-899	532	4	quality	quality	NOUN
ijassa-899	532	5	based	base	VERB
ijassa-899	532	6	on	on	ADP
ijassa-899	532	7	the	the	DET
ijassa-899	532	8	mape	mape	NOUN
ijassa-899	532	9	metric	metric	ADJ
ijassa-899	532	10	t	t	PROPN
ijassa-899	532	11	-	-	PUNCT
ijassa-899	532	12	mix	mix	NOUN
ijassa-899	532	13	xgboost	xgboost	ADP
ijassa-899	532	14	lgbm	lgbm	PROPN
ijassa-899	532	15	catboost	catboost	PROPN
ijassa-899	532	16	rf	rf	PROPN
ijassa-899	532	17	svm	svm	PROPN
ijassa-899	532	18	ann	ann	PROPN
ijassa-899	532	19	reservoir	reservoir	PROPN
ijassa-899	532	20	pressure	pressure	NOUN
ijassa-899	532	21	,	,	PUNCT
ijassa-899	532	22	mpa	mpa	PROPN
ijassa-899	532	23	8.13	8.13	NUM
ijassa-899	532	24	6.70	6.70	NUM
ijassa-899	532	25	6.56	6.56	NUM
ijassa-899	532	26	6.53	6.53	NUM
ijassa-899	532	27	8.31	8.31	NUM
ijassa-899	532	28	6.77	6.77	NUM
ijassa-899	532	29	9.63	9.63	NUM
ijassa-899	532	30	reservoir	reservoir	NOUN
ijassa-899	532	31	temperature	temperature	NOUN
ijassa-899	532	32	,	,	PUNCT
ijassa-899	532	33	oc	oc	VERB
ijassa-899	532	34	5.96	5.96	NUM
ijassa-899	532	35	5.50	5.50	NUM
ijassa-899	532	36	5.67	5.67	NUM
ijassa-899	532	37	5.75	5.75	NUM
ijassa-899	532	38	5.78	5.78	NUM
ijassa-899	532	39	6.58	6.58	NUM
ijassa-899	532	40	6.25	6.25	NUM
ijassa-899	532	41	surface	surface	NOUN
ijassa-899	532	42	gas	gas	NOUN
ijassa-899	532	43	density	density	NOUN
ijassa-899	532	44	,	,	PUNCT
ijassa-899	532	45	kg	kg	PROPN
ijassa-899	532	46	/	/	SYM
ijassa-899	532	47	m3	m3	PROPN
ijassa-899	532	48	3.42	3.42	NUM
ijassa-899	532	49	6.21	6.21	NUM
ijassa-899	532	50	6.20	6.20	NUM
ijassa-899	532	51	7.08	7.08	NUM
ijassa-899	532	52	6.23	6.23	NUM
ijassa-899	532	53	4.30	4.30	NUM
ijassa-899	532	54	7.38	7.38	NUM
ijassa-899	532	55	surface	surface	NOUN
ijassa-899	532	56	oil	oil	NOUN
ijassa-899	532	57	density	density	NOUN
ijassa-899	532	58	,	,	PUNCT
ijassa-899	532	59	kg	kg	PROPN
ijassa-899	532	60	/	/	SYM
ijassa-899	532	61	m3	m3	PROPN
ijassa-899	532	62	0.54	0.54	NUM
ijassa-899	532	63	0.87	0.87	NUM
ijassa-899	532	64	0.95	0.95	NUM
ijassa-899	532	65	0.88	0.88	NUM
ijassa-899	532	66	0.87	0.87	NUM
ijassa-899	532	67	0.67	0.67	NUM
ijassa-899	532	68	1.02	1.02	NUM
ijassa-899	532	69	gas	gas	NOUN
ijassa-899	532	70	content	content	NOUN
ijassa-899	532	71	,	,	PUNCT
ijassa-899	532	72	m3	m3	PROPN
ijassa-899	532	73	/	/	SYM
ijassa-899	532	74	t	t	PROPN
ijassa-899	532	75	3.92	3.92	NUM
ijassa-899	532	76	6.51	6.51	NUM
ijassa-899	532	77	6.85	6.85	NUM
ijassa-899	532	78	7.83	7.83	NUM
ijassa-899	532	79	7.33	7.33	NUM
ijassa-899	532	80	7.28	7.28	NUM
ijassa-899	532	81	9.03	9.03	NUM
ijassa-899	532	82	saturation	saturation	NOUN
ijassa-899	532	83	pressure	pressure	NOUN
ijassa-899	532	84	,	,	PUNCT
ijassa-899	532	85	mpa	mpa	PROPN
ijassa-899	532	86	11.69	11.69	NUM
ijassa-899	532	87	9.15	9.15	NUM
ijassa-899	532	88	9.25	9.25	NUM
ijassa-899	532	89	9.34	9.34	NUM
ijassa-899	532	90	10.33	10.33	NUM
ijassa-899	532	91	9.23	9.23	NUM
ijassa-899	532	92	18.07	18.07	NUM
ijassa-899	532	93	oil	oil	NOUN
ijassa-899	532	94	reservoir	reservoir	NOUN
ijassa-899	532	95	density	density	NOUN
ijassa-899	532	96	,	,	PUNCT
ijassa-899	532	97	kg	kg	PROPN
ijassa-899	532	98	/	/	SYM
ijassa-899	532	99	m3	m3	PROPN
ijassa-899	532	100	0.71	0.71	NUM
ijassa-899	532	101	1.35	1.35	NUM
ijassa-899	532	102	1.27	1.27	NUM
ijassa-899	532	103	2.04	2.04	NUM
ijassa-899	532	104	1.58	1.58	NUM
ijassa-899	532	105	1.77	1.77	NUM
ijassa-899	532	106	1.85	1.85	NUM
ijassa-899	532	107	volume	volume	NOUN
ijassa-899	532	108	coefficient	coefficient	NOUN
ijassa-899	532	109	.	.	PUNCT
ijassa-899	533	1	oil	oil	NOUN
ijassa-899	533	2	,	,	PUNCT
ijassa-899	533	3	m3	m3	PROPN
ijassa-899	533	4	/	/	SYM
ijassa-899	533	5	m3	m3	PROPN
ijassa-899	533	6	0.68	0.68	NUM
ijassa-899	533	7	1.43	1.43	NUM
ijassa-899	533	8	1.57	1.57	NUM
ijassa-899	533	9	2.70	2.70	NUM
ijassa-899	533	10	2.13	2.13	NUM
ijassa-899	533	11	2.23	2.23	NUM
ijassa-899	533	12	2.62	2.62	NUM
ijassa-899	533	13	reservoir	reservoir	NOUN
ijassa-899	533	14	oil	oil	NOUN
ijassa-899	533	15	viscosity	viscosity	NOUN
ijassa-899	533	16	,	,	PUNCT
ijassa-899	533	17	mpa	mpa	PROPN
ijassa-899	533	18	*	*	PUNCT
ijassa-899	533	19	s	s	PROPN
ijassa-899	533	20	22.32	22.32	NUM
ijassa-899	533	21	30.60	30.60	NUM
ijassa-899	533	22	26.86	26.86	NUM
ijassa-899	533	23	27.30	27.30	NUM
ijassa-899	533	24	57.60	57.60	NUM
ijassa-899	533	25	123.73	123.73	NUM
ijassa-899	533	26	9.66	9.66	NUM
ijassa-899	533	27	table	table	NOUN
ijassa-899	533	28	7.4	7.4	NUM
ijassa-899	533	29	.	.	PUNCT
ijassa-899	534	1	comparison	comparison	NOUN
ijassa-899	534	2	of	of	ADP
ijassa-899	534	3	prediction	prediction	NOUN
ijassa-899	534	4	quality	quality	NOUN
ijassa-899	534	5	based	base	VERB
ijassa-899	534	6	on	on	ADP
ijassa-899	534	7	the	the	DET
ijassa-899	534	8	rmspe	rmspe	PROPN
ijassa-899	534	9	metric	metric	ADJ
ijassa-899	534	10	t	t	NOUN
ijassa-899	534	11	-	-	PUNCT
ijassa-899	534	12	mix	mix	NOUN
ijassa-899	534	13	xgboost	xgboost	ADP
ijassa-899	534	14	lgbm	lgbm	PROPN
ijassa-899	534	15	catboost	catboost	PROPN
ijassa-899	534	16	rf	rf	PROPN
ijassa-899	534	17	svm	svm	PROPN
ijassa-899	534	18	ann	ann	PROPN
ijassa-899	534	19	reservoir	reservoir	PROPN
ijassa-899	534	20	pressure	pressure	NOUN
ijassa-899	534	21	,	,	PUNCT
ijassa-899	534	22	mpa	mpa	PROPN
ijassa-899	534	23	10.15	10.15	NUM
ijassa-899	534	24	9.05	9.05	NUM
ijassa-899	534	25	8.96	8.96	NUM
ijassa-899	534	26	8.56	8.56	NUM
ijassa-899	534	27	12.53	12.53	NUM
ijassa-899	534	28	8.86	8.86	NUM
ijassa-899	534	29	15.85	15.85	NUM
ijassa-899	534	30	reservoir	reservoir	NOUN
ijassa-899	534	31	temperature	temperature	NOUN
ijassa-899	534	32	,	,	PUNCT
ijassa-899	534	33	oc	oc	ADP
ijassa-899	534	34	7.50	7.50	NUM
ijassa-899	534	35	7.15	7.15	NUM
ijassa-899	534	36	7.26	7.26	NUM
ijassa-899	534	37	7.15	7.15	NUM
ijassa-899	534	38	7.61	7.61	NUM
ijassa-899	534	39	8.23	8.23	NUM
ijassa-899	534	40	7.93	7.93	NUM
ijassa-899	534	41	surface	surface	NOUN
ijassa-899	534	42	gas	gas	NOUN
ijassa-899	534	43	density	density	NOUN
ijassa-899	534	44	,	,	PUNCT
ijassa-899	534	45	kg	kg	PROPN
ijassa-899	534	46	/	/	SYM
ijassa-899	534	47	m3	m3	PROPN
ijassa-899	534	48	4.75	4.75	NUM
ijassa-899	534	49	7.80	7.80	NUM
ijassa-899	534	50	7.79	7.79	NUM
ijassa-899	534	51	8.72	8.72	NUM
ijassa-899	534	52	7.95	7.95	NUM
ijassa-899	534	53	5.44	5.44	NUM
ijassa-899	534	54	9.38	9.38	NUM
ijassa-899	534	55	surface	surface	NOUN
ijassa-899	534	56	oil	oil	NOUN
ijassa-899	534	57	density	density	NOUN
ijassa-899	534	58	,	,	PUNCT
ijassa-899	534	59	kg	kg	PROPN
ijassa-899	534	60	/	/	SYM
ijassa-899	534	61	m3	m3	PROPN
ijassa-899	534	62	0.88	0.88	NUM
ijassa-899	534	63	1.11	1.11	NUM
ijassa-899	534	64	1.21	1.21	NUM
ijassa-899	534	65	1.07	1.07	NUM
ijassa-899	534	66	1.12	1.12	NUM
ijassa-899	534	67	1.01	1.01	NUM
ijassa-899	534	68	1.58	1.58	NUM
ijassa-899	534	69	gas	gas	NOUN
ijassa-899	534	70	content	content	NOUN
ijassa-899	534	71	,	,	PUNCT
ijassa-899	534	72	m3	m3	PROPN
ijassa-899	534	73	/	/	SYM
ijassa-899	534	74	t	t	PROPN
ijassa-899	534	75	5.64	5.64	NUM
ijassa-899	534	76	8.57	8.57	NUM
ijassa-899	534	77	8.57	8.57	NUM
ijassa-899	534	78	9.92	9.92	NUM
ijassa-899	534	79	11.12	11.12	NUM
ijassa-899	534	80	10.15	10.15	NUM
ijassa-899	534	81	11.21	11.21	NUM
ijassa-899	534	82	saturation	saturation	NOUN
ijassa-899	534	83	pressure	pressure	NOUN
ijassa-899	534	84	,	,	PUNCT
ijassa-899	534	85	mpa	mpa	PROPN
ijassa-899	534	86	16.52	16.52	NUM
ijassa-899	534	87	12.79	12.79	NUM
ijassa-899	534	88	13.27	13.27	NUM
ijassa-899	534	89	12.57	12.57	NUM
ijassa-899	534	90	14.89	14.89	NUM
ijassa-899	534	91	12.71	12.71	NUM
ijassa-899	534	92	21.63	21.63	NUM
ijassa-899	534	93	oil	oil	NOUN
ijassa-899	534	94	reservoir	reservoir	NOUN
ijassa-899	534	95	density	density	NOUN
ijassa-899	534	96	,	,	PUNCT
ijassa-899	534	97	kg	kg	PROPN
ijassa-899	534	98	/	/	SYM
ijassa-899	534	99	m3	m3	PROPN
ijassa-899	534	100	1.47	1.47	NUM
ijassa-899	534	101	2.08	2.08	NUM
ijassa-899	534	102	1.78	1.78	NUM
ijassa-899	534	103	2.98	2.98	NUM
ijassa-899	534	104	2.36	2.36	NUM
ijassa-899	534	105	3.95	3.95	NUM
ijassa-899	534	106	2.37	2.37	NUM
ijassa-899	534	107	volume	volume	NOUN
ijassa-899	534	108	coefficient	coefficient	NOUN
ijassa-899	534	109	.	.	PUNCT
ijassa-899	535	1	oil	oil	NOUN
ijassa-899	535	2	,	,	PUNCT
ijassa-899	535	3	m3	m3	PROPN
ijassa-899	535	4	/	/	SYM
ijassa-899	535	5	m3	m3	PROPN
ijassa-899	535	6	1.49	1.49	NUM
ijassa-899	535	7	2.12	2.12	NUM
ijassa-899	535	8	2.36	2.36	NUM
ijassa-899	535	9	3.74	3.74	NUM
ijassa-899	535	10	3.34	3.34	NUM
ijassa-899	535	11	4.03	4.03	NUM
ijassa-899	535	12	3.29	3.29	NUM
ijassa-899	535	13	reservoir	reservoir	NOUN
ijassa-899	535	14	oil	oil	NOUN
ijassa-899	535	15	viscosity	viscosity	NOUN
ijassa-899	535	16	,	,	PUNCT
ijassa-899	535	17	mpa	mpa	PROPN
ijassa-899	535	18	*	*	PUNCT
ijassa-899	535	19	s	s	PART
ijassa-899	535	20	30.22	30.22	NUM
ijassa-899	535	21	44.11	44.11	NUM
ijassa-899	535	22	36.69	36.69	NUM
ijassa-899	535	23	36.82	36.82	NUM
ijassa-899	535	24	91.35	91.35	NUM
ijassa-899	535	25	398.20	398.20	NUM
ijassa-899	535	26	12.68	12.68	NUM
ijassa-899	535	27	8	8	NUM
ijassa-899	535	28	.	.	PUNCT
ijassa-899	536	1	conclusions	conclusion	NOUN
ijassa-899	536	2	the	the	DET
ijassa-899	536	3	probabilistic	probabilistic	ADJ
ijassa-899	536	4	model	model	NOUN
ijassa-899	536	5	of	of	ADP
ijassa-899	536	6	a	a	DET
ijassa-899	536	7	mixture	mixture	NOUN
ijassa-899	536	8	of	of	ADP
ijassa-899	536	9	multidimensional	multidimensional	ADJ
ijassa-899	536	10	student	student	NOUN
ijassa-899	536	11	distributions	distribution	NOUN
ijassa-899	536	12	proposed	propose	VERB
ijassa-899	536	13	in	in	ADP
ijassa-899	536	14	the	the	DET
ijassa-899	536	15	paper	paper	NOUN
ijassa-899	536	16	for	for	ADP
ijassa-899	536	17	describing	describe	VERB
ijassa-899	536	18	the	the	DET
ijassa-899	536	19	properties	property	NOUN
ijassa-899	536	20	of	of	ADP
ijassa-899	536	21	pvt	pvt	PROPN
ijassa-899	536	22	samples	sample	NOUN
ijassa-899	536	23	has	have	VERB
ijassa-899	536	24	a	a	DET
ijassa-899	536	25	wide	wide	ADJ
ijassa-899	536	26	range	range	NOUN
ijassa-899	536	27	of	of	ADP
ijassa-899	536	28	practical	practical	ADJ
ijassa-899	536	29	applications	application	NOUN
ijassa-899	536	30	,	,	PUNCT
ijassa-899	536	31	including	include	VERB
ijassa-899	536	32	checking	check	VERB
ijassa-899	536	33	the	the	DET
ijassa-899	536	34	samples	sample	NOUN
ijassa-899	536	35	for	for	ADP
ijassa-899	536	36	abnormality	abnormality	NOUN
ijassa-899	536	37	,	,	PUNCT
ijassa-899	536	38	dividing	divide	VERB
ijassa-899	536	39	the	the	DET
ijassa-899	536	40	samples	sample	NOUN
ijassa-899	536	41	into	into	ADP
ijassa-899	536	42	four	four	NUM
ijassa-899	536	43	clusters	cluster	NOUN
ijassa-899	536	44	,	,	PUNCT
ijassa-899	536	45	computing	compute	VERB
ijassa-899	536	46	recommended	recommend	VERB
ijassa-899	536	47	values	value	NOUN
ijassa-899	536	48	for	for	ADP
ijassa-899	536	49	missing	miss	VERB
ijassa-899	536	50	values	value	NOUN
ijassa-899	536	51	in	in	ADP
ijassa-899	536	52	the	the	DET
ijassa-899	536	53	sample	sample	NOUN
ijassa-899	536	54	,	,	PUNCT
ijassa-899	536	55	and	and	CCONJ
ijassa-899	536	56	in	in	ADP
ijassa-899	536	57	the	the	DET
ijassa-899	536	58	case	case	NOUN
ijassa-899	536	59	of	of	ADP
ijassa-899	536	60	sample	sample	NOUN
ijassa-899	536	61	abnormality	abnormality	NOUN
ijassa-899	536	62	–	–	PUNCT
ijassa-899	536	63	for	for	ADP
ijassa-899	536	64	all	all	DET
ijassa-899	536	65	features	feature	NOUN
ijassa-899	536	66	not	not	PART
ijassa-899	536	67	selected	select	VERB
ijassa-899	536	68	as	as	SCONJ
ijassa-899	536	69	trusted	trust	VERB
ijassa-899	536	70	.	.	PUNCT
ijassa-899	537	1	experiments	experiment	NOUN
ijassa-899	537	2	have	have	AUX
ijassa-899	537	3	shown	show	VERB
ijassa-899	537	4	that	that	SCONJ
ijassa-899	537	5	the	the	DET
ijassa-899	537	6	recommended	recommend	VERB
ijassa-899	537	7	values	value	NOUN
ijassa-899	537	8	obtained	obtain	VERB
ijassa-899	537	9	do	do	AUX
ijassa-899	537	10	not	not	PART
ijassa-899	537	11	contradict	contradict	VERB
ijassa-899	537	12	the	the	DET
ijassa-899	537	13	physical	physical	ADJ
ijassa-899	537	14	properties	property	NOUN
ijassa-899	537	15	of	of	ADP
ijassa-899	537	16	pvt	pvt	PROPN
ijassa-899	537	17	samples	sample	NOUN
ijassa-899	537	18	,	,	PUNCT
ijassa-899	537	19	in	in	ADP
ijassa-899	537	20	particular	particular	ADJ
ijassa-899	537	21	,	,	PUNCT
ijassa-899	537	22	they	they	PRON
ijassa-899	537	23	have	have	VERB
ijassa-899	537	24	smoothness	smoothness	ADJ
ijassa-899	537	25	in	in	ADP
ijassa-899	537	26	terms	term	NOUN
ijassa-899	537	27	of	of	ADP
ijassa-899	537	28	arguments	argument	NOUN
ijassa-899	537	29	.	.	PUNCT
ijassa-899	538	1	the	the	DET
ijassa-899	538	2	division	division	NOUN
ijassa-899	538	3	of	of	ADP
ijassa-899	538	4	samples	sample	NOUN
ijassa-899	538	5	into	into	ADP
ijassa-899	538	6	four	four	NUM
ijassa-899	538	7	clusters	cluster	NOUN
ijassa-899	538	8	corresponding	correspond	VERB
ijassa-899	538	9	to	to	ADP
ijassa-899	538	10	components	component	NOUN
ijassa-899	538	11	of	of	ADP
ijassa-899	538	12	the	the	DET
ijassa-899	538	13	multidimensional	multidimensional	ADJ
ijassa-899	538	14	student	student	NOUN
ijassa-899	538	15	mixture	mixture	NOUN
ijassa-899	538	16	is	be	AUX
ijassa-899	538	17	empirically	empirically	ADV
ijassa-899	538	18	justified	justify	VERB
ijassa-899	538	19	by	by	ADP
ijassa-899	538	20	comparison	comparison	NOUN
ijassa-899	538	21	with	with	ADP
ijassa-899	538	22	a	a	DET
ijassa-899	538	23	mixture	mixture	NOUN
ijassa-899	538	24	of	of	ADP
ijassa-899	538	25	multidimensional	multidimensional	ADJ
ijassa-899	538	26	normal	normal	ADJ
ijassa-899	538	27	distributions	distribution	NOUN
ijassa-899	538	28	.	.	PUNCT
ijassa-899	539	1	in	in	ADP
ijassa-899	539	2	the	the	DET
ijassa-899	539	3	latter	latter	ADJ
ijassa-899	539	4	case	case	NOUN
ijassa-899	539	5	,	,	PUNCT
ijassa-899	539	6	the	the	DET
ijassa-899	539	7	quality	quality	NOUN
ijassa-899	539	8	is	be	AUX
ijassa-899	539	9	unsatisfactory	unsatisfactory	ADJ
ijassa-899	539	10	due	due	ADP
ijassa-899	539	11	to	to	ADP
ijassa-899	539	12	the	the	DET
ijassa-899	539	13	presence	presence	NOUN
ijassa-899	539	14	of	of	ADP
ijassa-899	539	15	noise	noise	NOUN
ijassa-899	539	16	objects	object	NOUN
ijassa-899	539	17	that	that	SCONJ
ijassa-899	539	18	the	the	DET
ijassa-899	539	19	model	model	NOUN
ijassa-899	539	20	adjusts	adjust	VERB
ijassa-899	539	21	to	to	ADP
ijassa-899	539	22	a	a	DET
ijassa-899	539	23	separate	separate	ADJ
ijassa-899	539	24	cluster	cluster	NOUN
ijassa-899	539	25	.	.	PUNCT
ijassa-899	540	1	the	the	DET
ijassa-899	540	2	student	student	NOUN
ijassa-899	540	3	distribution	distribution	NOUN
ijassa-899	540	4	has	have	VERB
ijassa-899	540	5	heavier	heavy	ADJ
ijassa-899	540	6	tails	tail	NOUN
ijassa-899	540	7	,	,	PUNCT
ijassa-899	540	8	so	so	SCONJ
ijassa-899	540	9	it	it	PRON
ijassa-899	540	10	adjusts	adjust	VERB
ijassa-899	540	11	less	less	ADJ
ijassa-899	540	12	to	to	ADP
ijassa-899	540	13	emissions	emission	NOUN
ijassa-899	540	14	.	.	PUNCT
ijassa-899	541	1	the	the	DET
ijassa-899	541	2	probabilistic	probabilistic	ADJ
ijassa-899	541	3	model	model	NOUN
ijassa-899	541	4	has	have	VERB
ijassa-899	541	5	significant	significant	ADJ
ijassa-899	541	6	advantages	advantage	NOUN
ijassa-899	541	7	over	over	ADP
ijassa-899	541	8	other	other	ADJ
ijassa-899	541	9	models	model	NOUN
ijassa-899	541	10	since	since	SCONJ
ijassa-899	541	11	it	it	PRON
ijassa-899	541	12	can	can	AUX
ijassa-899	541	13	be	be	AUX
ijassa-899	541	14	used	use	VERB
ijassa-899	541	15	to	to	PART
ijassa-899	541	16	solve	solve	VERB
ijassa-899	541	17	several	several	ADJ
ijassa-899	541	18	problems	problem	NOUN
ijassa-899	541	19	at	at	ADP
ijassa-899	541	20	once	once	ADV
ijassa-899	541	21	and	and	CCONJ
ijassa-899	541	22	obtain	obtain	VERB
ijassa-899	541	23	consistent	consistent	ADJ
ijassa-899	541	24	results	result	NOUN
ijassa-899	541	25	.	.	PUNCT
ijassa-899	542	1	considering	consider	VERB
ijassa-899	542	2	only	only	ADV
ijassa-899	542	3	the	the	DET
ijassa-899	542	4	regression	regression	NOUN
ijassa-899	542	5	problem	problem	NOUN
ijassa-899	542	6	,	,	PUNCT
ijassa-899	542	7	the	the	DET
ijassa-899	542	8	probabilistic	probabilistic	ADJ
ijassa-899	542	9	model	model	NOUN
ijassa-899	542	10	,	,	PUNCT
ijassa-899	542	11	in	in	ADP
ijassa-899	542	12	contrast	contrast	NOUN
ijassa-899	542	13	to	to	ADP
ijassa-899	542	14	the	the	DET
ijassa-899	542	15	traditional	traditional	ADJ
ijassa-899	542	16	approach	approach	NOUN
ijassa-899	542	17	,	,	PUNCT
ijassa-899	542	18	allows	allow	VERB
ijassa-899	542	19	getting	get	VERB
ijassa-899	542	20	predictions	prediction	NOUN
ijassa-899	542	21	of	of	ADP
ijassa-899	542	22	set	set	NOUN
ijassa-899	542	23	of	of	ADP
ijassa-899	542	24	features	feature	NOUN
ijassa-899	542	25	for	for	ADP
ijassa-899	542	26	other	other	ADJ
ijassa-899	542	27	set	set	NOUN
ijassa-899	542	28	of	of	ADP
ijassa-899	542	29	features	feature	NOUN
ijassa-899	542	30	and	and	CCONJ
ijassa-899	542	31	vise	vise	ADJ
ijassa-899	542	32	verse	verse	NOUN
ijassa-899	542	33	without	without	ADP
ijassa-899	542	34	repeated	repeat	VERB
ijassa-899	542	35	model	model	NOUN
ijassa-899	542	36	training	training	NOUN
ijassa-899	542	37	.	.	PUNCT
ijassa-899	543	1	furthermore	furthermore	ADV
ijassa-899	543	2	,	,	PUNCT
ijassa-899	543	3	in	in	ADP
ijassa-899	543	4	the	the	DET
ijassa-899	543	5	conducted	conduct	VERB
ijassa-899	543	6	experiments	experiment	NOUN
ijassa-899	543	7	,	,	PUNCT
ijassa-899	543	8	the	the	DET
ijassa-899	543	9	quality	quality	NOUN
ijassa-899	543	10	of	of	ADP
ijassa-899	543	11	the	the	DET
ijassa-899	543	12	obtained	obtain	VERB
ijassa-899	543	13	predictions	prediction	NOUN
ijassa-899	543	14	is	be	AUX
ijassa-899	543	15	also	also	ADV
ijassa-899	543	16	superior	superior	ADJ
ijassa-899	543	17	to	to	ADP
ijassa-899	543	18	other	other	ADJ
ijassa-899	543	19	models	model	NOUN
ijassa-899	543	20	.	.	PUNCT
ijassa-899	544	1	references	reference	NOUN
ijassa-899	544	2	1	1	NUM
ijassa-899	544	3	.	.	PUNCT
ijassa-899	545	1	lagutin	lagutin	PROPN
ijassa-899	545	2	,	,	PUNCT
ijassa-899	545	3	m.	m.	PROPN
ijassa-899	545	4	b.	b.	PROPN
ijassa-899	545	5	(	(	PUNCT
ijassa-899	545	6	2009	2009	NUM
ijassa-899	545	7	)	)	PUNCT
ijassa-899	545	8	naglyadnaya	naglyadnaya	NOUN
ijassa-899	545	9	matematicheskaya	matematicheskaya	NOUN
ijassa-899	545	10	statistika	statistika	NOUN
ijassa-899	546	1	[	[	X
ijassa-899	546	2	visual	visual	ADJ
ijassa-899	546	3	mathematical	mathematical	ADJ
ijassa-899	546	4	statistics	statistic	NOUN
ijassa-899	546	5	]	]	PUNCT
ijassa-899	546	6	.	.	PUNCT
ijassa-899	547	1	moscow	moscow	PROPN
ijassa-899	547	2	,	,	PUNCT
ijassa-899	547	3	russia	russia	PROPN
ijassa-899	547	4	:	:	PUNCT
ijassa-899	547	5	binom	binom	PROPN
ijassa-899	547	6	.	.	PUNCT
ijassa-899	548	1	laboratoriya	laboratoriya	PROPN
ijassa-899	548	2	znanij	znanij	PROPN
ijassa-899	548	3	,	,	PUNCT
ijassa-899	549	1	[	[	X
ijassa-899	549	2	in	in	ADP
ijassa-899	549	3	russian	russian	PROPN
ijassa-899	549	4	]	]	PUNCT
ijassa-899	549	5	.	.	PUNCT
ijassa-899	550	1	2	2	X
ijassa-899	550	2	.	.	X
ijassa-899	550	3	kozlov	kozlov	PROPN
ijassa-899	550	4	m.v	m.v	PROPN
ijassa-899	550	5	.	.	PROPN
ijassa-899	550	6	,	,	PUNCT
ijassa-899	550	7	prohorov	prohorov	PROPN
ijassa-899	550	8	,	,	PUNCT
ijassa-899	550	9	a.	a.	PROPN
ijassa-899	550	10	v.	v.	PROPN
ijassa-899	550	11	(	(	PUNCT
ijassa-899	550	12	1987	1987	NUM
ijassa-899	550	13	)	)	PUNCT
ijassa-899	550	14	vvedenie	vvedenie	PROPN
ijassa-899	550	15	v	v	ADP
ijassa-899	550	16	matematicheskuyu	matematicheskuyu	PROPN
ijassa-899	550	17	statistiku	statistiku	NOUN
ijassa-899	551	1	[	[	X
ijassa-899	551	2	introduction	introduction	NOUN
ijassa-899	551	3	to	to	ADP
ijassa-899	551	4	mathematical	mathematical	ADJ
ijassa-899	551	5	statistics	statistic	NOUN
ijassa-899	551	6	]	]	PUNCT
ijassa-899	551	7	.	.	PUNCT
ijassa-899	552	1	moscow	moscow	PROPN
ijassa-899	552	2	,	,	PUNCT
ijassa-899	552	3	ussr	ussr	PROPN
ijassa-899	552	4	:	:	PUNCT
ijassa-899	552	5	msu	msu	PROPN
ijassa-899	552	6	,	,	PUNCT
ijassa-899	552	7	[	[	X
ijassa-899	552	8	in	in	ADP
ijassa-899	552	9	russian	russian	PROPN
ijassa-899	552	10	]	]	PUNCT
ijassa-899	552	11	.	.	PUNCT
ijassa-899	553	1	3	3	X
ijassa-899	553	2	.	.	X
ijassa-899	553	3	shiryaev	shiryaev	PROPN
ijassa-899	553	4	,	,	PUNCT
ijassa-899	553	5	a.	a.	PROPN
ijassa-899	553	6	n.	n.	PROPN
ijassa-899	553	7	(	(	PUNCT
ijassa-899	553	8	2004	2004	NUM
ijassa-899	553	9	)	)	PUNCT
ijassa-899	554	1	veroyatnost	veroyatnost	ADV
ijassa-899	555	1	[	[	X
ijassa-899	555	2	probability	probability	NOUN
ijassa-899	555	3	]	]	X
ijassa-899	555	4	.	.	PUNCT
ijassa-899	556	1	moscow	moscow	PROPN
ijassa-899	556	2	,	,	PUNCT
ijassa-899	556	3	russia	russia	PROPN
ijassa-899	556	4	:	:	PUNCT
ijassa-899	556	5	mcnmo	mcnmo	NOUN
ijassa-899	556	6	,	,	PUNCT
ijassa-899	556	7	[	[	X
ijassa-899	556	8	in	in	ADP
ijassa-899	556	9	russian	russian	NOUN
ijassa-899	556	10	]	]	PUNCT
ijassa-899	556	11	.	.	PUNCT
ijassa-899	557	1	copyright	copyright	NOUN
ijassa-899	557	2	©	©	PROPN
ijassa-899	557	3	2020	2020	NUM
ijassa-899	557	4	assa	assa	NOUN
ijassa-899	557	5	.	.	PUNCT
ijassa-899	558	1	adv	adv	PROPN
ijassa-899	558	2	syst	syst	PROPN
ijassa-899	558	3	sci	sci	PROPN
ijassa-899	558	4	appl	appl	PROPN
ijassa-899	558	5	(	(	PUNCT
ijassa-899	558	6	2020	2020	NUM
ijassa-899	558	7	)	)	PUNCT
ijassa-899	558	8	118	118	NUM
ijassa-899	558	9	n.a	n.a	PROPN
ijassa-899	558	10	.	.	PROPN
ijassa-899	558	11	volkov	volkov	PROPN
ijassa-899	558	12	,	,	PUNCT
ijassa-899	558	13	e.yu	e.yu	PROPN
ijassa-899	558	14	.	.	PROPN
ijassa-899	558	15	dakhova	dakhova	PROPN
ijassa-899	558	16	,	,	PUNCT
ijassa-899	558	17	s.a	s.a	PROPN
ijassa-899	558	18	.	.	PROPN
ijassa-899	558	19	budennyy	budennyy	PROPN
ijassa-899	558	20	,	,	PUNCT
ijassa-899	558	21	a.m.	a.m.	PROPN
ijassa-899	558	22	andrianova	andrianova	PROPN
ijassa-899	559	1	4	4	X
ijassa-899	559	2	.	.	PUNCT
ijassa-899	559	3	kotz	kotz	PROPN
ijassa-899	559	4	,	,	PUNCT
ijassa-899	559	5	s.	s.	PROPN
ijassa-899	559	6	,	,	PUNCT
ijassa-899	559	7	nadarajah	nadarajah	PROPN
ijassa-899	559	8	,	,	PUNCT
ijassa-899	559	9	s.	s.	PROPN
ijassa-899	559	10	(	(	PUNCT
ijassa-899	559	11	2004	2004	NUM
ijassa-899	559	12	)	)	PUNCT
ijassa-899	559	13	.	.	PUNCT
ijassa-899	560	1	multivariate	multivariate	PROPN
ijassa-899	560	2	t	t	PROPN
ijassa-899	560	3	-	-	PUNCT
ijassa-899	560	4	distributions	distribution	NOUN
ijassa-899	560	5	and	and	CCONJ
ijassa-899	560	6	their	their	PRON
ijassa-899	560	7	applications	application	NOUN
ijassa-899	560	8	.	.	PUNCT
ijassa-899	561	1	cambridge	cambridge	PROPN
ijassa-899	561	2	:	:	PUNCT
ijassa-899	561	3	cambridge	cambridge	PROPN
ijassa-899	561	4	university	university	PROPN
ijassa-899	561	5	press	press	NOUN
ijassa-899	561	6	.	.	PUNCT
ijassa-899	562	1	doi:10.1017	doi:10.1017	NOUN
ijassa-899	562	2	/	/	SYM
ijassa-899	562	3	cbo9780511550683	cbo9780511550683	NOUN
ijassa-899	562	4	5	5	NUM
ijassa-899	562	5	.	.	PUNCT
ijassa-899	562	6	kibria	kibria	PROPN
ijassa-899	562	7	,	,	PUNCT
ijassa-899	562	8	b.	b.	PROPN
ijassa-899	562	9	m.	m.	PROPN
ijassa-899	562	10	g.	g.	PROPN
ijassa-899	562	11	,	,	PUNCT
ijassa-899	562	12	joarder	joarder	NOUN
ijassa-899	562	13	,	,	PUNCT
ijassa-899	562	14	a.	a.	PROPN
ijassa-899	562	15	h.	h.	PROPN
ijassa-899	562	16	(	(	PUNCT
ijassa-899	562	17	2006	2006	NUM
ijassa-899	562	18	)	)	PUNCT
ijassa-899	562	19	.	.	PUNCT
ijassa-899	563	1	a	a	DET
ijassa-899	563	2	short	short	ADJ
ijassa-899	563	3	review	review	NOUN
ijassa-899	563	4	of	of	ADP
ijassa-899	563	5	multivariate	multivariate	NOUN
ijassa-899	563	6	t	t	NOUN
ijassa-899	563	7	-	-	PUNCT
ijassa-899	563	8	distribution	distribution	NOUN
ijassa-899	563	9	.	.	PUNCT
ijassa-899	564	1	journal	journal	NOUN
ijassa-899	564	2	of	of	ADP
ijassa-899	564	3	statistical	statistical	ADJ
ijassa-899	564	4	research	research	NOUN
ijassa-899	564	5	issn	issn	NOUN
ijassa-899	564	6	.	.	PROPN
ijassa-899	564	7	40	40	NUM
ijassa-899	564	8	.	.	PUNCT
ijassa-899	565	1	256	256	NUM
ijassa-899	565	2	-	-	SYM
ijassa-899	565	3	422	422	NUM
ijassa-899	565	4	.	.	NOUN
ijassa-899	566	1	6	6	NUM
ijassa-899	566	2	.	.	X
ijassa-899	566	3	bishop	bishop	PROPN
ijassa-899	566	4	,	,	PUNCT
ijassa-899	566	5	c.	c.	PROPN
ijassa-899	566	6	m.	m.	NOUN
ijassa-899	566	7	(	(	PUNCT
ijassa-899	566	8	2006	2006	NUM
ijassa-899	566	9	)	)	PUNCT
ijassa-899	566	10	.	.	PUNCT
ijassa-899	567	1	pattern	pattern	NOUN
ijassa-899	567	2	recognition	recognition	NOUN
ijassa-899	567	3	and	and	CCONJ
ijassa-899	567	4	machine	machine	NOUN
ijassa-899	567	5	learning	learning	NOUN
ijassa-899	567	6	.	.	PUNCT
ijassa-899	568	1	springer	springer	PROPN
ijassa-899	568	2	.	.	PUNCT
ijassa-899	569	1	isbn	isbn	PROPN
ijassa-899	569	2	9780	9780	NUM
ijassa-899	569	3	-	-	SYM
ijassa-899	569	4	387	387	NUM
ijassa-899	569	5	-	-	PUNCT
ijassa-899	569	6	31073	31073	NUM
ijassa-899	569	7	-	-	SYM
ijassa-899	569	8	2	2	NUM
ijassa-899	569	9	.	.	NOUN
ijassa-899	569	10	7	7	NUM
ijassa-899	569	11	.	.	X
ijassa-899	569	12	peel	peel	NOUN
ijassa-899	569	13	,	,	PUNCT
ijassa-899	569	14	d.	d.	PROPN
ijassa-899	569	15	,	,	PUNCT
ijassa-899	569	16	mclachlan	mclachlan	PROPN
ijassa-899	569	17	,	,	PUNCT
ijassa-899	569	18	g	g	PROPN
ijassa-899	569	19	..	..	PUNCT
ijassa-899	569	20	(	(	PUNCT
ijassa-899	569	21	2000	2000	NUM
ijassa-899	569	22	)	)	PUNCT
ijassa-899	569	23	.	.	PUNCT
ijassa-899	570	1	robust	robust	ADJ
ijassa-899	570	2	mixture	mixture	NOUN
ijassa-899	570	3	modelling	modelling	NOUN
ijassa-899	570	4	using	use	VERB
ijassa-899	570	5	the	the	DET
ijassa-899	570	6	t	t	NOUN
ijassa-899	570	7	distribution	distribution	NOUN
ijassa-899	570	8	.	.	PUNCT
ijassa-899	571	1	stat	stat	PROPN
ijassa-899	571	2	comput	comput	PROPN
ijassa-899	571	3	.	.	PUNCT
ijassa-899	572	1	10	10	NUM
ijassa-899	572	2	.	.	X
ijassa-899	572	3	10.1023	10.1023	NUM
ijassa-899	572	4	/	/	SYM
ijassa-899	572	5	a:1008981510081	a:1008981510081	PROPN
ijassa-899	572	6	.	.	PUNCT
ijassa-899	573	1	8	8	NUM
ijassa-899	573	2	.	.	PUNCT
ijassa-899	574	1	shoham	shoham	PROPN
ijassa-899	574	2	s.	s.	PROPN
ijassa-899	574	3	,	,	PUNCT
ijassa-899	574	4	fellows	fellows	PROPN
ijassa-899	574	5	m.	m.	NOUN
ijassa-899	574	6	,	,	PUNCT
ijassa-899	574	7	normann	normann	PROPN
ijassa-899	574	8	r.	r.	PROPN
ijassa-899	574	9	(	(	PUNCT
ijassa-899	574	10	2003	2003	NUM
ijassa-899	574	11	)	)	PUNCT
ijassa-899	574	12	.	.	PUNCT
ijassa-899	575	1	robust	robust	ADJ
ijassa-899	575	2	,	,	PUNCT
ijassa-899	575	3	automatic	automatic	ADJ
ijassa-899	575	4	spike	spike	NOUN
ijassa-899	575	5	sorting	sorting	NOUN
ijassa-899	575	6	using	use	VERB
ijassa-899	575	7	mixtures	mixture	NOUN
ijassa-899	575	8	of	of	ADP
ijassa-899	575	9	multivariate	multivariate	NOUN
ijassa-899	575	10	t	t	PROPN
ijassa-899	575	11	-	-	PUNCT
ijassa-899	575	12	distributions	distribution	NOUN
ijassa-899	575	13	.	.	PUNCT
ijassa-899	576	1	journal	journal	NOUN
ijassa-899	576	2	of	of	ADP
ijassa-899	576	3	neuroscience	neuroscience	NOUN
ijassa-899	576	4	methods	method	NOUN
ijassa-899	576	5	.	.	PUNCT
ijassa-899	577	1	127	127	NUM
ijassa-899	577	2	.	.	X
ijassa-899	577	3	111	111	NUM
ijassa-899	577	4	-	-	SYM
ijassa-899	577	5	22	22	NUM
ijassa-899	577	6	.	.	PUNCT
ijassa-899	578	1	10.1016	10.1016	NUM
ijassa-899	578	2	/	/	SYM
ijassa-899	578	3	s0165	s0165	VERB
ijassa-899	578	4	-	-	PUNCT
ijassa-899	578	5	0270(03)00120	0270(03)00120	NOUN
ijassa-899	578	6	-	-	PUNCT
ijassa-899	578	7	1	1	NUM
ijassa-899	578	8	.	.	NOUN
ijassa-899	578	9	9	9	NUM
ijassa-899	578	10	.	.	X
ijassa-899	578	11	bishop	bishop	PROPN
ijassa-899	578	12	c.	c.	PROPN
ijassa-899	578	13	m.	m.	PROPN
ijassa-899	578	14	,	,	PUNCT
ijassa-899	578	15	svensen	svensen	PROPN
ijassa-899	578	16	m	m	PROPN
ijassa-899	578	17	..	..	PUNCT
ijassa-899	578	18	(	(	PUNCT
ijassa-899	578	19	2004	2004	NUM
ijassa-899	578	20	)	)	PUNCT
ijassa-899	578	21	.	.	PUNCT
ijassa-899	579	1	robust	robust	ADJ
ijassa-899	579	2	bayesian	bayesian	NOUN
ijassa-899	579	3	mixture	mixture	NOUN
ijassa-899	579	4	modelling	modelling	NOUN
ijassa-899	579	5	.	.	PUNCT
ijassa-899	580	1	neurocomputing	neurocompute	VERB
ijassa-899	580	2	.	.	PUNCT
ijassa-899	581	1	64	64	NUM
ijassa-899	581	2	.	.	NOUN
ijassa-899	581	3	235	235	NUM
ijassa-899	581	4	-	-	SYM
ijassa-899	581	5	252	252	NUM
ijassa-899	581	6	.	.	PUNCT
ijassa-899	582	1	10.1016	10.1016	NUM
ijassa-899	582	2	/	/	SYM
ijassa-899	582	3	j.neucom.2004.11.018	j.neucom.2004.11.018	PROPN
ijassa-899	582	4	.	.	PUNCT
ijassa-899	583	1	10	10	NUM
ijassa-899	583	2	.	.	PUNCT
ijassa-899	584	1	eaton	eaton	PROPN
ijassa-899	584	2	m.	m.	PROPN
ijassa-899	584	3	l.	l.	PROPN
ijassa-899	584	4	(	(	PUNCT
ijassa-899	584	5	1983	1983	NUM
ijassa-899	584	6	)	)	PUNCT
ijassa-899	584	7	.	.	PUNCT
ijassa-899	585	1	multivariate	multivariate	NOUN
ijassa-899	585	2	statistics	statistic	NOUN
ijassa-899	585	3	:	:	PUNCT
ijassa-899	585	4	a	a	DET
ijassa-899	585	5	vector	vector	NOUN
ijassa-899	585	6	space	space	NOUN
ijassa-899	585	7	approach	approach	NOUN
ijassa-899	585	8	.	.	PUNCT
ijassa-899	586	1	john	john	PROPN
ijassa-899	586	2	wiley	wiley	PROPN
ijassa-899	586	3	and	and	CCONJ
ijassa-899	586	4	sons	son	NOUN
ijassa-899	586	5	.	.	PUNCT
ijassa-899	587	1	pp	pp	ADV
ijassa-899	587	2	.	.	PUNCT
ijassa-899	588	1	116–117	116–117	NUM
ijassa-899	588	2	.	.	PUNCT
ijassa-899	589	1	isbn	isbn	ADJ
ijassa-899	589	2	978	978	NUM
ijassa-899	589	3	-	-	SYM
ijassa-899	589	4	0	0	NUM
ijassa-899	589	5	-	-	PUNCT
ijassa-899	589	6	471	471	NUM
ijassa-899	589	7	-	-	PUNCT
ijassa-899	589	8	02776	02776	NUM
ijassa-899	589	9	-	-	PUNCT
ijassa-899	589	10	8	8	NUM
ijassa-899	589	11	.	.	NOUN
ijassa-899	589	12	11	11	NUM
ijassa-899	589	13	.	.	PUNCT
ijassa-899	590	1	gantmaher	gantmaher	PROPN
ijassa-899	590	2	f.	f.	PROPN
ijassa-899	590	3	r.	r.	PROPN
ijassa-899	590	4	(	(	PUNCT
ijassa-899	590	5	2010	2010	NUM
ijassa-899	590	6	)	)	PUNCT
ijassa-899	590	7	teoriya	teoriya	NOUN
ijassa-899	590	8	matric	matric	NOUN
ijassa-899	591	1	[	[	X
ijassa-899	591	2	matrix	matrix	NOUN
ijassa-899	591	3	theory	theory	NOUN
ijassa-899	591	4	]	]	X
ijassa-899	591	5	.	.	PUNCT
ijassa-899	592	1	moscow	moscow	PROPN
ijassa-899	592	2	,	,	PUNCT
ijassa-899	592	3	russia	russia	PROPN
ijassa-899	592	4	:	:	PUNCT
ijassa-899	592	5	fizmatlit	fizmatlit	ADJ
ijassa-899	592	6	,	,	PUNCT
ijassa-899	592	7	[	[	X
ijassa-899	592	8	in	in	ADP
ijassa-899	592	9	russian	russian	PROPN
ijassa-899	592	10	]	]	PUNCT
ijassa-899	592	11	.	.	PUNCT
ijassa-899	593	1	12	12	NUM
ijassa-899	593	2	.	.	PUNCT
ijassa-899	594	1	fruhwirth	fruhwirth	NOUN
ijassa-899	594	2	-	-	PUNCT
ijassa-899	594	3	schnatter	schnatter	NOUN
ijassa-899	594	4	s.	s.	PROPN
ijassa-899	594	5	(	(	PUNCT
ijassa-899	594	6	2006	2006	NUM
ijassa-899	594	7	)	)	PUNCT
ijassa-899	594	8	finite	finite	NOUN
ijassa-899	594	9	mixture	mixture	NOUN
ijassa-899	594	10	and	and	CCONJ
ijassa-899	594	11	markov	markov	NOUN
ijassa-899	594	12	switching	switching	NOUN
ijassa-899	594	13	models	model	NOUN
ijassa-899	594	14	.	.	PUNCT
ijassa-899	595	1	psychometrika	psychometrika	X
ijassa-899	595	2	.	.	PUNCT
ijassa-899	596	1	74	74	NUM
ijassa-899	596	2	.	.	X
ijassa-899	597	1	559	559	NUM
ijassa-899	597	2	-	-	SYM
ijassa-899	597	3	560	560	NUM
ijassa-899	597	4	.	.	PUNCT
ijassa-899	597	5	10.1007	10.1007	NUM
ijassa-899	597	6	/	/	SYM
ijassa-899	597	7	s11336	s11336	NOUN
ijassa-899	597	8	-	-	PUNCT
ijassa-899	597	9	009	009	NUM
ijassa-899	597	10	-	-	PUNCT
ijassa-899	597	11	9121	9121	NUM
ijassa-899	597	12	-	-	PUNCT
ijassa-899	597	13	4	4	NUM
ijassa-899	597	14	.	.	NOUN
ijassa-899	597	15	13	13	NUM
ijassa-899	597	16	.	.	PUNCT
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ijassa-899	598	2	w.	w.	PROPN
ijassa-899	598	3	b.	b.	PROPN
ijassa-899	598	4	,	,	PUNCT
ijassa-899	598	5	hocking	hocking	PROPN
ijassa-899	598	6	r.	r.	PROPN
ijassa-899	598	7	r.	r.	PROPN
ijassa-899	598	8	(	(	PUNCT
ijassa-899	598	9	1972	1972	NUM
ijassa-899	598	10	)	)	PUNCT
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ijassa-899	598	12	as	as	ADP
ijassa-899	598	13	53	53	NUM
ijassa-899	598	14	:	:	PUNCT
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ijassa-899	598	16	variate	variate	PROPN
ijassa-899	598	17	generator	generator	PROPN
ijassa-899	598	18	.	.	PUNCT
ijassa-899	599	1	applied	apply	VERB
ijassa-899	599	2	statistics	statistic	NOUN
ijassa-899	599	3	,	,	PUNCT
ijassa-899	599	4	21	21	NUM
ijassa-899	599	5	,	,	PUNCT
ijassa-899	599	6	pp	pp	ADJ
ijassa-899	599	7	.	.	PUNCT
ijassa-899	600	1	341	341	NUM
ijassa-899	600	2	-	-	SYM
ijassa-899	600	3	345	345	NUM
ijassa-899	600	4	.	.	PUNCT
ijassa-899	601	1	14	14	NUM
ijassa-899	601	2	.	.	PUNCT
ijassa-899	602	1	thomas	thomas	PROPN
ijassa-899	602	2	p.	p.	PROPN
ijassa-899	602	3	m.	m.	NOUN
ijassa-899	602	4	(	(	PUNCT
ijassa-899	602	5	1997	1997	NUM
ijassa-899	602	6	)	)	PUNCT
ijassa-899	602	7	.	.	PUNCT
ijassa-899	603	1	old	old	ADJ
ijassa-899	603	2	and	and	CCONJ
ijassa-899	603	3	new	new	ADJ
ijassa-899	603	4	matrix	matrix	NOUN
ijassa-899	603	5	algebra	algebra	NOUN
ijassa-899	603	6	useful	useful	ADJ
ijassa-899	603	7	for	for	ADP
ijassa-899	603	8	statistics	statistic	NOUN
ijassa-899	603	9	.	.	PUNCT
ijassa-899	604	1	mit	mit	PROPN
ijassa-899	604	2	media	medium	NOUN
ijassa-899	604	3	lab	lab	NOUN
ijassa-899	604	4	note	note	NOUN
ijassa-899	604	5	.	.	PUNCT
ijassa-899	605	1	15	15	X
ijassa-899	605	2	.	.	PUNCT
ijassa-899	605	3	brusilovskij	brusilovskij	PROPN
ijassa-899	605	4	a.	a.	PROPN
ijassa-899	605	5	i.	i.	PROPN
ijassa-899	605	6	(	(	PUNCT
ijassa-899	605	7	2002	2002	NUM
ijassa-899	605	8	)	)	PUNCT
ijassa-899	605	9	fazovye	fazovye	NOUN
ijassa-899	605	10	prevrashcheniya	prevrashcheniya	NOUN
ijassa-899	605	11	pri	pri	NOUN
ijassa-899	605	12	razrabotke	razrabotke	NOUN
ijassa-899	605	13	mestorozhdenij	mestorozhdenij	NOUN
ijassa-899	605	14	nefti	nefti	VERB
ijassa-899	605	15	i	i	PRON
ijassa-899	605	16	gaza	gaza	PROPN
ijassa-899	606	1	[	[	X
ijassa-899	606	2	phase	phase	NOUN
ijassa-899	606	3	transformations	transformation	VERB
ijassa-899	606	4	in	in	ADP
ijassa-899	606	5	the	the	DET
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ijassa-899	606	7	of	of	ADP
ijassa-899	606	8	oil	oil	NOUN
ijassa-899	606	9	and	and	CCONJ
ijassa-899	606	10	gas	gas	NOUN
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ijassa-899	606	12	]	]	PUNCT
ijassa-899	606	13	.	.	PUNCT
ijassa-899	607	1	moscow	moscow	PROPN
ijassa-899	607	2	,	,	PUNCT
ijassa-899	607	3	russia	russia	PROPN
ijassa-899	607	4	:	:	PUNCT
ijassa-899	607	5	graal	graal	PROPN
ijassa-899	607	6	,	,	PUNCT
ijassa-899	607	7	[	[	X
ijassa-899	607	8	in	in	ADP
ijassa-899	607	9	russian	russian	PROPN
ijassa-899	607	10	]	]	PUNCT
ijassa-899	607	11	.	.	PUNCT
ijassa-899	608	1	16	16	NUM
ijassa-899	608	2	.	.	PUNCT
ijassa-899	609	1	alakbari	alakbari	PROPN
ijassa-899	609	2	f.	f.	PROPN
ijassa-899	609	3	,	,	PUNCT
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ijassa-899	609	5	s.	s.	PROPN
ijassa-899	609	6	,	,	PUNCT
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ijassa-899	609	8	s	s	PROPN
ijassa-899	609	9	..	..	PUNCT
ijassa-899	609	10	(	(	PUNCT
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ijassa-899	609	12	)	)	PUNCT
ijassa-899	609	13	.	.	PUNCT
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ijassa-899	610	11	.	.	PUNCT
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ijassa-899	611	2	.	.	PUNCT
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ijassa-899	612	11	,	,	PUNCT
ijassa-899	612	12	10.2118/184208	10.2118/184208	PROPN
ijassa-899	612	13	-	-	PUNCT
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ijassa-899	612	17	.	.	PUNCT
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ijassa-899	613	5	,	,	PUNCT
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ijassa-899	613	7	,	,	PUNCT
ijassa-899	613	8	i.	i.	PROPN
ijassa-899	613	9	i.	i.	PROPN
ijassa-899	613	10	,	,	PUNCT
ijassa-899	613	11	ikiensikimama	ikiensikimama	PROPN
ijassa-899	613	12	,	,	PUNCT
ijassa-899	613	13	s.	s.	PROPN
ijassa-899	613	14	s.	s.	PROPN
ijassa-899	613	15	(	(	PUNCT
ijassa-899	613	16	2013	2013	NUM
ijassa-899	613	17	)	)	PUNCT
ijassa-899	613	18	.	.	PUNCT
ijassa-899	614	1	bubble	bubble	NOUN
ijassa-899	614	2	point	point	NOUN
ijassa-899	614	3	pressure	pressure	NOUN
ijassa-899	614	4	prediction	prediction	NOUN
ijassa-899	614	5	model	model	NOUN
ijassa-899	614	6	for	for	ADP
ijassa-899	614	7	niger	niger	NOUN
ijassa-899	614	8	delta	delta	NOUN
ijassa-899	614	9	crude	crude	NOUN
ijassa-899	614	10	using	use	VERB
ijassa-899	614	11	artificial	artificial	ADJ
ijassa-899	614	12	neural	neural	ADJ
ijassa-899	614	13	network	network	NOUN
ijassa-899	614	14	approach	approach	NOUN
ijassa-899	614	15	.	.	PUNCT
ijassa-899	615	1	society	society	NOUN
ijassa-899	615	2	of	of	ADP
ijassa-899	615	3	petroleum	petroleum	NOUN
ijassa-899	615	4	engineers	engineer	NOUN
ijassa-899	615	5	.	.	PUNCT
ijassa-899	616	1	doi:10.2118/167586	doi:10.2118/167586	ADJ
ijassa-899	616	2	-	-	PUNCT
ijassa-899	616	3	ms	ms	NOUN
ijassa-899	616	4	18	18	NUM
ijassa-899	616	5	.	.	PUNCT
ijassa-899	617	1	alcocer	alcocer	PROPN
ijassa-899	617	2	y.	y.	PROPN
ijassa-899	617	3	,	,	PUNCT
ijassa-899	617	4	patricia	patricia	PROPN
ijassa-899	617	5	r	r	PROPN
ijassa-899	617	6	..	..	PUNCT
ijassa-899	617	7	(	(	PUNCT
ijassa-899	617	8	2001	2001	NUM
ijassa-899	617	9	)	)	PUNCT
ijassa-899	617	10	.	.	PUNCT
ijassa-899	618	1	neural	neural	ADJ
ijassa-899	618	2	networks	network	NOUN
ijassa-899	618	3	models	model	NOUN
ijassa-899	618	4	for	for	ADP
ijassa-899	618	5	estimation	estimation	NOUN
ijassa-899	618	6	of	of	ADP
ijassa-899	618	7	fluid	fluid	ADJ
ijassa-899	618	8	properties	property	NOUN
ijassa-899	618	9	.	.	PUNCT
ijassa-899	619	1	proc	proc	NOUN
ijassa-899	619	2	.	.	PUNCT
ijassa-899	620	1	of	of	ADP
ijassa-899	620	2	the	the	DET
ijassa-899	620	3	spe	spe	PROPN
ijassa-899	620	4	latin	latin	ADJ
ijassa-899	620	5	american	american	ADJ
ijassa-899	620	6	and	and	CCONJ
ijassa-899	620	7	caribbean	caribbean	ADJ
ijassa-899	620	8	petroleum	petroleum	NOUN
ijassa-899	620	9	engineering	engineering	NOUN
ijassa-899	620	10	conference	conference	NOUN
ijassa-899	620	11	,	,	PUNCT
ijassa-899	620	12	10.2523/69624	10.2523/69624	NUM
ijassa-899	620	13	-	-	PUNCT
ijassa-899	620	14	ms	ms	NOUN
ijassa-899	620	15	.	.	PROPN
ijassa-899	620	16	19	19	NUM
ijassa-899	620	17	.	.	PUNCT
ijassa-899	620	18	osman	osman	PROPN
ijassa-899	620	19	,	,	PUNCT
ijassa-899	620	20	e.	e.	PROPN
ijassa-899	620	21	a.	a.	PROPN
ijassa-899	620	22	,	,	PUNCT
ijassa-899	620	23	abdel	abdel	NOUN
ijassa-899	620	24	-	-	PUNCT
ijassa-899	620	25	wahhab	wahhab	PROPN
ijassa-899	620	26	,	,	PUNCT
ijassa-899	620	27	o.	o.	PROPN
ijassa-899	620	28	a.	a.	PROPN
ijassa-899	620	29	,	,	PUNCT
ijassa-899	620	30	al	al	PROPN
ijassa-899	620	31	-	-	PUNCT
ijassa-899	620	32	marhoun	marhoun	PROPN
ijassa-899	620	33	,	,	PUNCT
ijassa-899	620	34	m.	m.	NOUN
ijassa-899	620	35	a.	a.	NOUN
ijassa-899	620	36	(	(	PUNCT
ijassa-899	620	37	2001	2001	NUM
ijassa-899	620	38	)	)	PUNCT
ijassa-899	620	39	.	.	PUNCT
ijassa-899	621	1	prediction	prediction	NOUN
ijassa-899	621	2	of	of	ADP
ijassa-899	621	3	oil	oil	NOUN
ijassa-899	621	4	pvt	pvt	PROPN
ijassa-899	621	5	properties	property	NOUN
ijassa-899	621	6	using	use	VERB
ijassa-899	621	7	neural	neural	ADJ
ijassa-899	621	8	networks	network	NOUN
ijassa-899	621	9	.	.	PUNCT
ijassa-899	622	1	society	society	NOUN
ijassa-899	622	2	of	of	ADP
ijassa-899	622	3	petroleum	petroleum	NOUN
ijassa-899	622	4	engineers	engineer	NOUN
ijassa-899	622	5	.	.	PUNCT
ijassa-899	623	1	doi:10.2118/68233ms	doi:10.2118/68233ms	PROPN
ijassa-899	623	2	20	20	NUM
ijassa-899	623	3	.	.	PUNCT
ijassa-899	624	1	el	el	PROPN
ijassa-899	624	2	-	-	PROPN
ijassa-899	624	3	sebakhy	sebakhy	NOUN
ijassa-899	624	4	,	,	PUNCT
ijassa-899	624	5	e.	e.	PROPN
ijassa-899	624	6	a.	a.	PROPN
ijassa-899	624	7	,	,	PUNCT
ijassa-899	624	8	sheltami	sheltami	NOUN
ijassa-899	624	9	,	,	PUNCT
ijassa-899	624	10	t.	t.	PROPN
ijassa-899	624	11	,	,	PUNCT
ijassa-899	624	12	al	al	PROPN
ijassa-899	624	13	-	-	PUNCT
ijassa-899	624	14	bokhitan	bokhitan	PROPN
ijassa-899	624	15	,	,	PUNCT
ijassa-899	624	16	s.	s.	PROPN
ijassa-899	624	17	y.	y.	PROPN
ijassa-899	624	18	,	,	PUNCT
ijassa-899	624	19	shaaban	shaaban	ADJ
ijassa-899	624	20	,	,	PUNCT
ijassa-899	624	21	y.	y.	PROPN
ijassa-899	624	22	,	,	PUNCT
ijassa-899	624	23	raharja	raharja	PROPN
ijassa-899	624	24	et	et	PROPN
ijassa-899	624	25	.	.	PUNCT
ijassa-899	625	1	al	al	PROPN
ijassa-899	625	2	.	.	PROPN
ijassa-899	626	1	(	(	PUNCT
ijassa-899	626	2	2007	2007	NUM
ijassa-899	626	3	)	)	PUNCT
ijassa-899	626	4	.	.	PUNCT
ijassa-899	627	1	support	support	NOUN
ijassa-899	627	2	vector	vector	NOUN
ijassa-899	627	3	machines	machine	NOUN
ijassa-899	627	4	framework	framework	NOUN
ijassa-899	627	5	for	for	ADP
ijassa-899	627	6	predicting	predict	VERB
ijassa-899	627	7	the	the	DET
ijassa-899	627	8	pvt	pvt	PROPN
ijassa-899	627	9	properties	property	NOUN
ijassa-899	627	10	of	of	ADP
ijassa-899	627	11	crude	crude	ADJ
ijassa-899	627	12	oil	oil	NOUN
ijassa-899	627	13	systems	system	NOUN
ijassa-899	627	14	.	.	PUNCT
ijassa-899	628	1	society	society	NOUN
ijassa-899	628	2	of	of	ADP
ijassa-899	628	3	petroleum	petroleum	NOUN
ijassa-899	628	4	engineers	engineer	NOUN
ijassa-899	628	5	.	.	PUNCT
ijassa-899	629	1	doi:10.2118/105698	doi:10.2118/105698	ADJ
ijassa-899	629	2	-	-	PUNCT
ijassa-899	629	3	ms	ms	NOUN
ijassa-899	629	4	copyright	copyright	NOUN
ijassa-899	629	5	©	©	PROPN
ijassa-899	629	6	2020	2020	NUM
ijassa-899	629	7	assa	assa	NOUN
ijassa-899	629	8	.	.	PUNCT
ijassa-899	630	1	adv	adv	PROPN
ijassa-899	630	2	syst	syst	PROPN
ijassa-899	630	3	sci	sci	PROPN
ijassa-899	630	4	appl	appl	PROPN
ijassa-899	630	5	(	(	PUNCT
ijassa-899	630	6	2020	2020	NUM
ijassa-899	630	7	)	)	PUNCT
ijassa-899	630	8	introduction	introduction	NOUN
ijassa-899	630	9	distributions	distribution	NOUN
ijassa-899	630	10	and	and	CCONJ
ijassa-899	630	11	their	their	PRON
ijassa-899	630	12	properties	property	NOUN
ijassa-899	630	13	normal	normal	ADJ
ijassa-899	630	14	distribution	distribution	NOUN
ijassa-899	630	15	gamma	gamma	NOUN
ijassa-899	630	16	distribution	distribution	NOUN
ijassa-899	630	17	student	student	NOUN
ijassa-899	630	18	distribution	distribution	NOUN
ijassa-899	630	19	marginal	marginal	ADJ
ijassa-899	630	20	distributions	distribution	NOUN
ijassa-899	630	21	conditional	conditional	ADJ
ijassa-899	630	22	distribution	distribution	NOUN
ijassa-899	630	23	mixture	mixture	NOUN
ijassa-899	630	24	model	model	NOUN
ijassa-899	630	25	properties	property	NOUN
ijassa-899	630	26	of	of	ADP
ijassa-899	630	27	a	a	DET
ijassa-899	630	28	mixture	mixture	NOUN
ijassa-899	630	29	model	model	NOUN
ijassa-899	630	30	normal	normal	ADJ
ijassa-899	630	31	mixture	mixture	NOUN
ijassa-899	630	32	distribution	distribution	NOUN
ijassa-899	630	33	student	student	NOUN
ijassa-899	630	34	mixture	mixture	NOUN
ijassa-899	630	35	distribution	distribution	NOUN
ijassa-899	630	36	formulas	formula	NOUN
ijassa-899	630	37	derivation	derivation	NOUN
ijassa-899	630	38	for	for	ADP
ijassa-899	630	39	estimation	estimation	NOUN
ijassa-899	630	40	the	the	DET
ijassa-899	630	41	parameters	parameter	NOUN
ijassa-899	630	42	of	of	ADP
ijassa-899	630	43	student	student	NOUN
ijassa-899	630	44	mixture	mixture	NOUN
ijassa-899	630	45	e	e	NOUN
ijassa-899	630	46	-	-	NOUN
ijassa-899	630	47	step	step	NOUN
ijassa-899	630	48	,	,	PUNCT
ijassa-899	630	49	internal	internal	ADJ
ijassa-899	630	50	step	step	NOUN
ijassa-899	630	51	i	i	NOUN
ijassa-899	630	52	e	e	NOUN
ijassa-899	630	53	-	-	NOUN
ijassa-899	630	54	step	step	NOUN
ijassa-899	630	55	,	,	PUNCT
ijassa-899	630	56	internal	internal	ADJ
ijassa-899	630	57	step	step	NOUN
ijassa-899	630	58	ii	ii	PROPN
ijassa-899	630	59	m	m	NOUN
ijassa-899	630	60	-	-	PUNCT
ijassa-899	630	61	step	step	NOUN
ijassa-899	630	62	variational	variational	ADJ
ijassa-899	630	63	lower	lower	ADV
ijassa-899	630	64	bound	bind	VERB
ijassa-899	630	65	and	and	CCONJ
ijassa-899	630	66	convergence	convergence	NOUN
ijassa-899	630	67	of	of	ADP
ijassa-899	630	68	the	the	DET
ijassa-899	630	69	method	method	NOUN
ijassa-899	630	70	applications	application	NOUN
ijassa-899	630	71	of	of	ADP
ijassa-899	630	72	probabilistic	probabilistic	ADJ
ijassa-899	630	73	model	model	NOUN
ijassa-899	630	74	clustering	clustering	NOUN
ijassa-899	630	75	anomalies	anomaly	NOUN
ijassa-899	630	76	missing	miss	VERB
ijassa-899	630	77	data	datum	NOUN
ijassa-899	630	78	conditional	conditional	ADJ
ijassa-899	630	79	distribution	distribution	NOUN
ijassa-899	630	80	and	and	CCONJ
ijassa-899	630	81	probabilistic	probabilistic	ADJ
ijassa-899	630	82	regression	regression	NOUN
ijassa-899	630	83	on	on	ADP
ijassa-899	630	84	features	feature	NOUN
ijassa-899	630	85	modeling	model	VERB
ijassa-899	630	86	pvt	pvt	PROPN
ijassa-899	630	87	properties	property	NOUN
ijassa-899	630	88	of	of	ADP
ijassa-899	630	89	reservoir	reservoir	NOUN
ijassa-899	630	90	fluids	fluid	NOUN
ijassa-899	630	91	using	use	VERB
ijassa-899	630	92	a	a	DET
ijassa-899	630	93	probabilistic	probabilistic	ADJ
ijassa-899	630	94	model	model	NOUN
ijassa-899	630	95	data	datum	NOUN
ijassa-899	630	96	description	description	NOUN
ijassa-899	630	97	normal	normal	ADJ
ijassa-899	630	98	mixture	mixture	NOUN
ijassa-899	630	99	model	model	NOUN
ijassa-899	630	100	student	student	NOUN
ijassa-899	630	101	mixture	mixture	NOUN
ijassa-899	630	102	model	model	NOUN
ijassa-899	630	103	model	model	PROPN
ijassa-899	630	104	research	research	NOUN
ijassa-899	630	105	artificial	artificial	ADJ
ijassa-899	630	106	experiments	experiment	NOUN
ijassa-899	630	107	predictions	prediction	NOUN
ijassa-899	630	108	quality	quality	NOUN
ijassa-899	630	109	conclusions	conclusion	NOUN